1 Introduction

In our dataset, we have different datatypes: - Whole genome sequencing at shallow depth (sWGS) - Agilent 180K arrays - Illumina HumanCytoSNP data

These needs to be harmonised first so that they can be read into R.

1.1 sWGS

The fastq files were preprocessed with ./code/preprocessing/sWGS_pipeline_SE.snakefile, which runs bwa-mem, picard markduplicates and WisecondorX/ichorCNA. The output (bins.bed, segments.bed) from WisecondorX were further used in the downstream processing. The reference for WisecondorX was generated from an in-house dataset of healthy volunteers. More information on how to obtain a reference dataset for WisecondorX is available at https://github.com/CenterForMedicalGeneticsGhent/WisecondorX. The PoN and supporting files for ichorCNA were obtained from the ichorCNA repository (https://github.com/broadinstitute/ichorCNA).

1.2 WES

The fastq files were preprocessed with ./code/preprocessing/sWGS_pipeline_PE.snakefile, which runs bwa-mem, picard markduplicates and WisecondorX/ichorCNA.

CNVkit was run with the parameters described in cnvkit.sh on the deduplicated bam files with the paired germline sample. Target region bed files (e.g. SureSelect) was downloaded from the Agilent website. The link between the ID from germline WES and ID from tumor WES is also in the cnvkit.sh file.

1.3 Illumina HumanCytoSNP

Illumina Genomestudio 2.0 (https://www.illumina.com/techniques/microarrays/array-data-analysis-experimental-design/genomestudio.html) was used to obtain the log2ratio (Robs/Rexp) per bin from the IDAT files. The SampleSheets are available in ./resources/. The other files (.egt and .bpm are too large to host here and are available on the Illumina website (e.g. https://emea.support.illumina.com/downloads/humancytosnp-12v2-1_product_files-ns.html)

1.4 Agilent 180K array

Raw data for the Agilent 180K arrays was not available. The processed data (sample_raw.csv) was used for downstream processing. The data was processed according to the methods in https://pubmed.ncbi.nlm.nih.gov/23308108/.

1.5 Integrating the different platforms

./code/convertRaw.snakefile: converts data from obtained from WES, sWGS, Illumina HumanCytoSNP-12 or 180K array (Agilent) into a structure that allows the comparison of these different data-types, and harmonises the bin size between all data types. Also uses DNAcopy to obtain segmentations. If necessary (i.e. array data), the files are liftover to GRCh38.

Example input files are present in the ./examples/ folder.

1.6 Other scripts

./code/makeBins.sh obtain bins of choice starting from .fa.fai file, is a dependency of ./code/convertRaw.snakefile

./code/DNAcopy_segment.R: runs DNAcopy, is a dependency of ./code/convertRaw.snakefile

1.7 Load dependencies and packages

# the original CPA from Raman et al.
get.cpa <- function(seg){
   cpa <- sum(abs(seg$zscore) * (seg$end - seg$start  + 1)) / nrow(seg) * 1e-8 # per 100 Mb
   return(cpa)
}

# The modified CPA score (CPAm)
get.cpa.modified2 <- function(seg){
   cpa <- sum(abs(seg$ratio) * (seg$end - seg$start + 1)) / 1e8
   return(cpa)
}

makeCNVcomparison <- function(datadir1, datadir2, sample1, sample2, patientID, uniqueID, binsize, tumor_input, tumortype, tissueplatform){
   
   print(paste0("cfDNA sample: ", sample1))
   print(paste0("tumorDNA sample: ", sample2))
   print(paste0("binsize: ", binsize))
   
   
   bins_top <- read_tsv(paste0(datadir1, str_subset(dir(datadir1, pattern = sample1), "bins_mask")), col_types = c("fdd?d"),
                        col_names = c("chr", "start", "end", "id", "ratio"), skip = 1)
   bins_top <- bins_top %>% filter(!chr %in% c("X", "Y", "23", "24"))
   
   segs_top <- read_tsv(paste0(datadir1, str_subset(dir(datadir1, pattern = sample1), "segments_mask.bed")), col_types = c("fdddd"),
                        col_names = c("chr", "start", "end", "ratio", "zscore"))
   segs_top <- segs_top %>% filter(!chr %in% c("X", "Y", "23", "24")) %>% mutate(Sample = sample1)
   
   if (tumor_input == "array_BE" | tumor_input == "array_CZ" | tumor_input == "WES_FR" ){
      bins_bottom <-  read_tsv(paste0(datadir2,"/",sample2,"_", binsize, ".tsv"), col_types = c("fddd"),
                               col_names = c("chr", "start", "end", "ratio"))
      bins_bottom <- bins_bottom %>% filter(!chr %in% c("X", "Y", "23", "24"))
      bins_bottom$chr <- factor(bins_bottom$chr, levels = chr_order)
      
      segs_bottom <- read_tsv(paste0(datadir2,"/",sample2,"_", binsize, "_segments.tsv"), col_types = c("fddd"),
                              col_names = c("chr", "start", "end", "ratio"), skip = 1)
      
      segs_bottom <- segs_bottom %>% filter(!chr %in% c("X", "Y", "23", "24")) %>% mutate(Sample = sample2)
      segs_bottom$chr <- factor(segs_bottom$chr, levels = chr_order)
   } else if (tumor_input == "sWGS"){
      bins_bottom <-  read_tsv(paste0(datadir2, str_subset(dir(datadir2, pattern = sample2), "bins_mask")), col_types = c("fdd?d"),
                               col_names = c("chr", "start", "end", "id", "ratio"))
      bins_bottom <- bins_bottom %>% filter(!chr %in% c("X", "Y", "23", "24"))
      bins_bottom$chr <- factor(bins_bottom$chr, levels = chr_order)
      
      segs_bottom <- read_tsv(paste0(datadir2, str_subset(dir(datadir2, pattern = sample2), "segments_mask.bed")), col_types = c("fdddd"),
                              col_names = c("chr", "start", "end", "ratio", "zscore"))
      segs_bottom <- segs_bottom %>% filter(!chr %in% c("X", "Y", "23", "24")) %>% mutate(Sample = sample2)
      segs_bottom$chr <- factor(segs_bottom$chr, levels = chr_order)
   }
   
   color_top <- wes_palette("Cavalcanti1")[1]
   color_bottom <- wes_palette("Cavalcanti1")[4]
   color_abberations <- wes_palette("Cavalcanti1")[5]
   
   max.ratio_bins <- max(c(bins_top$ratio, bins_bottom$ratio), na.rm = TRUE)
   min.ratio_bins <- min(c(bins_top$ratio, bins_bottom$ratio), na.rm = TRUE)
   
   max.ratio_bins.top <- max(c(bins_top$ratio), na.rm = TRUE)
   min.ratio_bins.top <- min(c(bins_top$ratio), na.rm = TRUE)
   
   max.ratio_bins.bottom <- max(c(bins_bottom$ratio), na.rm = TRUE)
   min.ratio_bins.bottom <- min(c(bins_bottom$ratio), na.rm = TRUE)
   
   
   if (tumor_input == "sWGS"){
      cpa_top <- round(get.cpa(segs_top),4)
      cpa_bottom <- round(get.cpa(segs_bottom),4)
      cpa_top <- round(get.cpa.modified2(segs_top),4)
      cpa_bottom <- round(get.cpa.modified2(segs_bottom),4)
      cpa.calc <- "CPAm"
   } else{
      cpa_top <- round(get.cpa.modified2(segs_top),4)
      cpa_bottom <- round(get.cpa.modified2(segs_bottom),4)
      cpa.calc <- "CPAm"
   }
   
   if (tumortype == "neuroblastoma"){
      # add MYCN region
      nbl_1 <- geom_segment(data = segs_top %>% filter(chr == "2"), aes(x=15940550, xend=15947004, y=-Inf, yend=Inf), col = "grey", alpha = 0.4) 
      nbl_2 <- geom_text(data = segs_top %>% filter(chr == "2"), label = "MYCN", x=65947004, y=max.ratio_bins*0.9, angle = 90, alpha = 0.4, size = 3, color = "grey")
   } else {
      nbl_1 <- NULL
      nbl_2 <- NULL
   }
   
   ptop <- ggplot(bins_top, aes(x = start, y = ratio)) + 
      theme_bw() + 
      labs(title = paste0(patientID, " - sWGS cfDNA - ", sample1, " - ", cpa.calc,": ", cpa_top, " - ", tumortype), y = "log2(ratio)") + 
      geom_point(size = 0.3, col = color_top, alpha = 0.7) +
      lims(y = c(min.ratio_bins,max.ratio_bins))+
      theme(panel.spacing.x = unit(0, "lines"),
            panel.spacing.y = unit(0, "lines"),
            axis.title.x =  element_blank(),
            axis.text.x =  element_blank(),
            axis.ticks.x =  element_blank(),
            strip.background = element_rect(color = "white", fill = "white"),
            #   panel.border = element_rect(color = "gray", fill = NA, size = 0.3), 
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.background = element_blank()) + 
      geom_hline(yintercept = 0, linetype = "dashed", col = "gray") +
      facet_wrap(~chr, strip.position = "bottom", scales ="free_x", nrow = 1) + 
      geom_segment(data = segs_top, aes(x = start, xend = end, y = ratio , yend = ratio), col = color_abberations) +
      geom_rect(data = segs_top, aes(xmin=start, xmax=end, ymin=0, ymax=ratio), fill = color_abberations, alpha = 0.4) + nbl_1 + nbl_2
   
   
   pbottom <- ggplot(bins_bottom, aes(x = start, y = ratio)) + 
      theme_bw() + 
      labs(title = paste0(patientID, " - ", tissueplatform, " tissue - ", sample2, " - ",cpa.calc, ": ", cpa_bottom, " - ", tumortype), y = "log2(ratio)") +  
      geom_point(size = 0.3, col = color_bottom, alpha = 0.7) +
      lims(y = c(min.ratio_bins,max.ratio_bins))+
      theme(panel.spacing.x = unit(0, "lines"),
            panel.spacing.y = unit(0, "lines"),
            axis.title.x =  element_blank(),
            axis.text.x =  element_blank(),
            axis.ticks.x =  element_blank(),
            strip.background = element_rect(color = "white", fill = "white"),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.background = element_blank()) + 
      geom_hline(yintercept = 0, linetype = "dashed", col = "gray") +
      facet_wrap(~chr, strip.position = "bottom", scales ="free_x", nrow = 1) +
      geom_segment(data = segs_bottom, aes(x = start, xend = end, y = ratio , yend = ratio), col = color_abberations) +
      geom_rect(data = segs_bottom, aes(xmin=start, xmax=end, ymin=0, ymax=ratio), fill = color_abberations, alpha = 0.4) + nbl_1 + nbl_2
   
   
   corplot_df_bins <- inner_join(bins_top, bins_bottom, by = c("chr", "start", "end"))
   #corplot_df <- corplot_df %>% filter(!is.na(ratio.x) & !is.na(ratio.y))
   
   corplot_df_bins <- corplot_df_bins %>%
      mutate(rM.top=rollapply(ratio.x,100, FUN=function(x) mean(x, na.rm=TRUE), fill=NA, align="right")) %>%
      mutate(rM.bottom=rollapply(ratio.y,100, FUN=function(x) mean(x, na.rm=TRUE), fill=NA, align="right")) 
   
   rollmean <- ggplot(tibble(corplot_df_bins), aes(x = start)) + 
      theme_bw() + 
      labs(y = "log2(ratio)", x = "chromosomes") +
      geom_line(aes(y=rM.bottom), col = color_bottom, alpha = 1, size = 1) +
      geom_line(aes(y=rM.top), col = color_top, alpha = 0.7, size = 1) +
      theme(panel.spacing.x = unit(0, "lines"),
            panel.spacing.y = unit(0, "lines"),
            #  axis.title.x =  element_blank(),
            axis.text.x =  element_blank(),
            axis.ticks.x =  element_blank(),
            strip.background = element_rect(color = "white", fill = "white"),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.background = element_blank()) + 
      geom_hline(yintercept = 0, linetype = "dashed", col = "gray") +
      facet_wrap(~chr, strip.position = "bottom", scales ="free_x", nrow = 1) + lims(y = c(-NA,NA))+ nbl_1
   
   max.ratio <- max(c(corplot_df_bins$ratio.x, corplot_df_bins$ratio.y), na.rm = TRUE)
   min.ratio <- min(c(corplot_df_bins$ratio.x, corplot_df_bins$ratio.y), na.rm = TRUE)
   corplot <- ggplot(corplot_df_bins, aes(x = ratio.x, y = ratio.y)) + 
      geom_point(size = 0.3, col = wes_palette("Royal1")[1]) + 
      theme_bw() +
      geom_abline(slope = 1, intercept = 0) +
      stat_cor(method = "pearson", aes(label = ..r.label..)) +
      geom_smooth(method = "lm", col = color_abberations, linetype = "dashed") +
      lims(x = c(min.ratio , max.ratio), y = c(min.ratio, max.ratio)) + 
      labs(x = "log2(ratio) cfDNA", y = "log2(ratio) tissue") +
      theme(axis.title.x = element_text(color = color_top),
            axis.title.y = element_text(color = color_bottom))
   
   R <- cor(corplot_df_bins$ratio.x, corplot_df_bins$ratio.y, use = "complete.obs", method = "pearson")
   
   CNVplot <- ggarrange(ptop, pbottom, nrow = 2, ncol = 1, align = "v", heights = c(1, 1))
   comparisons <- ggarrange(rollmean, corplot, nrow = 1, ncol = 2, widths = c(0.7, 0.3))
   
   arrangeplot <- ggarrange(CNVplot, comparisons, nrow = 2, heights = c(0.75, 0.25), widths = c(1, 0.90), align = "hv")
   ggsave(paste0(plotfolder, patientID, "_", sample1, "_", sample2, "_", binsize, ".png"), plot = arrangeplot, width = 11, height = 9, dpi = 300)
   
   df_comparison <- data.frame(UniqueID = c(uniqueID), PatientID = c(patientID), CFD_ID = c(sample1), tumorDNA_ID = c(sample2), pearsonR = c(R), cpa_cfDNA = c(cpa_top), cpa_tumorDNA = c(cpa_bottom), cpa_type = c(cpa.calc))
   return(df_comparison)
}

2 Sample annotation

The sample annotation is read from the xlsx file.

sample_annotation <- read_excel("/Users/rmvpaeme/Dropbox (speleman lab)/Basecamp/CVP/LQB pediatric patients/analysis_RVP/sample_annotation.xlsx")
sample_annotation <- sample_annotation %>% filter(cfDNA_data_available == "TRUE" & tumorDNA_data_available == "TRUE")
sample_annotation_full <- sample_annotation
sample_annotation <- sample_annotation %>% filter(is.na(filtersample))

sample_annotation$TumorGroup <- ifelse(
   sample_annotation$TumorType %in% c("Ewing sarcoma", "osteosarcoma"),
   "bone tumor",
   ifelse(
      sample_annotation$TumorType %in% c(
         "nephroblastoma",
         "malignant rhabdoid tumor of the kidney",
         "renal cell carcinoma",
         "kidney sarcoma"
      ),
      "kidney tumor",
      ifelse(
         sample_annotation$TumorType %in% c("neuroblastoma", "ganglioneuroblastoma"),
         "neuroblastoma",
         ifelse(
            sample_annotation$TumorType %in% c("rhabdomyosarcoma"),
            "rhabdomyosarcoma", "brain tumor"
         )
      )
   )
)


sample_annotation$TumorAbbrev <- sample_annotation$TumorType
sample_annotation$TumorAbbrev  <- gsub("Ewing sarcoma", "EWS", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("osteosarcoma", "OS", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("astrocytic pilocytoma", "ASP", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("ependymoma", "EPA", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("ganglioglioma", "GGA", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("ganglioneuroblastoma", "GNA", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("glioblastoma", "GBA", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("hemangioblastoma", "HGA", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("kidney sarcoma", "KS", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("meningioma", "MGA", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("malignant rhabdoid tumor of the kidney", "MRT", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("nephroblastoma", "NFB", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("neuroblastoma", "NBL", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("renal cell carcinoma", "RCC", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("rhabdomyosarcoma", "RMS", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("medulloblastoma", "MBL", sample_annotation$TumorAbbrev)

sample_annotation$cfDNA_HMW_ratio <- as.numeric(sample_annotation$cfDNA_prct_conc)/as.numeric(sample_annotation$HMW_prct_conc)
sample_annotation$cfDNA_HMW_ratio <- as.numeric(sample_annotation$cfDNA_HMW_ratio)


sample_annotation$quality_score <- ifelse(sample_annotation$cfDNA_HMW_ratio < 1, "low",
                                          ifelse(sample_annotation$cfDNA_HMW_ratio < 5 & sample_annotation$cfDNA_HMW_ratio > 1, "medium", "high"))

sample_annotation$cfDNA_prct <- as.numeric(sample_annotation$cfDNA_prct_conc)/(as.numeric(sample_annotation$HMW_prct_conc)+as.numeric(sample_annotation$cfDNA_prct_conc))
sample_annotation$cfDNA_HMW_ratio <- as.numeric(sample_annotation$cfDNA_HMW_ratio)


tumorSamples <- sample_annotation %>% select(UniqueID, PatientID, tumorDNA_ID, TumorType,tumorDNA_assay,tumorDNA_assay_detail, tumorDNA_biomaterial, TumorGroup,cfDNA_HMW_ratio )
colnames(tumorSamples) <- c("UniqueID", "PatientID",  "SampleID", "TumorType", "assay", "assayDetail", "source", "TumorGroup", "cfDNA_HMW_ratio")
tumorSamples$biomaterial <- "tumor DNA"

cfDNASamples <-  sample_annotation %>% select(UniqueID, PatientID, CFD_ID, TumorType,tumorDNA_assay,tumorDNA_assay_detail, CFD_biomaterial, TumorGroup, cfDNA_HMW_ratio) 
colnames(cfDNASamples) <- c("UniqueID", "PatientID",  "SampleID", "TumorType", "assay", "assayDetail", "source", "TumorGroup", "cfDNA_HMW_ratio")
cfDNASamples$assay <- "sWGS"
cfDNASamples$assayDetail <- "sWGS"
cfDNASamples$biomaterial <- "cfDNA"

sample_annotation_long <- bind_rows(tumorSamples, cfDNASamples)

4 Reading in all data

While reading in the data, the loop makes a comparative figure for every comparison in sample_annotation, for example:

comparison

comparison

if (!file.exists("./data/compareAll.tsv")){
   cfDNAvsTissue <- data.frame()
   #sample_annotation <- sample_annotation  %>% filter(SampleOrigin == "MH")
   for (row in 1:nrow(sample_annotation)){
      sample1 <- sample_annotation[row,]$CFD_ID
      sample2 <- sample_annotation[row,]$tumorDNA_ID
      patientID <-  sample_annotation[row,]$PatientID
      uniqueID <-  sample_annotation[row,]$UniqueID
      tissueplatform <- sample_annotation[row,]$tumorDNA_assay_detail
      tumortype <- sample_annotation[row,]$TumorType
      datadir1 <- datadir_sWGS_cfDNA
      if (sample_annotation[row,]$tumorDNA_assay == "array_BE"){
         input_tumor = "array_BE"
         datadir2 <-  datadir_arrayBE
      } else if (sample_annotation[row,]$tumorDNA_assay == "array_CZ"){
         input_tumor = "array_CZ"
         datadir2 <-  datadir_arrayCZ
      } else if (sample_annotation[row,]$tumorDNA_assay == "WES_FR"){
         input_tumor = "WES_FR"
         datadir2 <-  datadir_WES
      } else if (sample_annotation[row,]$tumorDNA_assay == "sWGS"){
         input_tumor = "sWGS"
         datadir2 <-  datadir_sWGS_tissue
      }
      if (length(dir(datadir2, pattern = sample2) > 0  | length(dir(datadir1, pattern = sample1) > 0))) {
         tmp <- makeCNVcomparison(datadir1, datadir2, sample1, sample2, patientID, uniqueID, "200kb", input_tumor, tumortype,tissueplatform)
         cfDNAvsTissue <- rbind(cfDNAvsTissue, tmp)
      }
      print(paste0("Processed: ", row, "/", nrow(sample_annotation)))
   }
   write_tsv(cfDNAvsTissue, "./data/compareAll.tsv")
} else {
   cfDNAvsTissue  <- read_tsv("./data/compareAll.tsv")
}

a <- cfDNAvsTissue %>% select(PatientID,  UniqueID, CFD_ID, cpa_cfDNA, pearsonR)
colnames(a) <- c("PatientID", "UniqueID","SampleID", "CPAm", "pearsonR")
a$biomaterial <- "cfDNA"

b <- cfDNAvsTissue %>% select(PatientID, UniqueID, tumorDNA_ID, cpa_tumorDNA, pearsonR )
colnames(b) <-  c("PatientID", "UniqueID","SampleID", "CPAm", "pearsonR")
b$biomaterial <- "tumor DNA"

a <- rbind(a,b)
sample_annotation_long <- left_join(sample_annotation_long, a)
a <- NULL
b <- NULL


cfDNAvsTissue <- merge(sample_annotation, cfDNAvsTissue, by = c("PatientID", "UniqueID", "PatientID", "CFD_ID", "tumorDNA_ID"))


normal_CPA <- read_csv("./data/sWGS_healthy/CPA_all.csv") %>% dplyr::select(-CPAm) %>% melt() 
colnames(normal_CPA) <- c("CFD_ID","cpa_type", "cpa_cfDNA")

normal_CPAm <- read_csv("./data/sWGS_healthy/CPA_all.csv") %>% dplyr::select(-CPA) %>% melt() 
colnames(normal_CPAm) <- c("CFD_ID","cpa_type", "cpa_cfDNA")
normal_CPAm$cpa_tumorDNA <- normal_CPA$cpa_cfDNA
normal_CPAm$TumorType <- "healthy"
normal_CPAm$CFD_biomaterial <- "plasma"
normal_CPAm$SampleOrigin <- "UZG"
normal_CPAm$cfDNA_HMW_ratio <- 0
cfDNAvsTissue_withHealthy <- bind_rows(cfDNAvsTissue, normal_CPAm)

5 Calculation of the 1% FDR for CPAm/CPA values

A normal distribution is fitted to the CPAm values, and based on the mean and SD, the qnorm(.99) is determined to obtain the 1% FDR ratio.

The 1% FDR for CPAm is 0.3549618 and the 1% FDR for CPA is 1.5344485.

library(geiger)
binsize = "200kb"

makeLog2comparison <- function(datadir1, datadir2, sample1, sample2, patientID){
   
   print(paste0("cfDNA sample: ", sample1))
   print(paste0("tumorDNA sample: ", sample2))
   print(paste0("binsize: ", binsize))
   
   
   tmp_A <- read_tsv(paste0(datadir1, str_subset(dir(datadir1, pattern = sample1), "segments_per_200kb_mask.tsv")), col_types = c("fddd"),
                     col_names = c("chr", "start", "end", "ratio_cfDNA"), skip = 1)
   tmp_A <- tmp_A %>% filter(!chr %in% c("X", "Y", "23", "24")) 
   
   
   segs_A <- read_tsv(paste0(datadir1, str_subset(dir(datadir1, pattern = sample1), "segments_mask.bed")), col_types = c("fdddd"),
                      col_names = c("chr", "start", "end", "ratio", "zscore"))
   segs_A <- segs_A %>% filter(!chr %in% c("X", "Y", "23", "24"))
   
   tmp_B <- read_tsv(paste0(datadir2, str_subset(dir(datadir2, pattern = sample2), "segments_per_200kb_mask.tsv")), col_types = c("fddd"),
                     col_names = c("chr", "start", "end", "ratio_tumor"), skip = 1)
   tmp_B <- tmp_B %>% filter(!chr %in% c("X", "Y", "23", "24")) 
   
   if (tumor_input == "array_BE" | tumor_input == "array_CZ" | tumor_input == "WES_FR"){
      segs_B <- read_tsv(paste0(datadir2,"/",sample2,"_", binsize, "_segments.tsv"), col_types = c("fddd"),
                         col_names = c("chr", "start", "end", "ratio"), skip = 1)
      
      segs_B <- segs_B %>% filter(!chr %in% c("X", "Y", "23", "24")) 
   } else if (tumor_input == "sWGS"){
      segs_B <- read_tsv(paste0(datadir2, str_subset(dir(datadir2, pattern = sample2), "segments_mask.bed")), col_types = c("fdddd"),
                         col_names = c("chr", "start", "end", "ratio", "zscore"))
      segs_B <- segs_B %>% filter(!chr %in% c("X", "Y", "23", "24")) 
   }
   # 
   # segs_B <- read_tsv(paste0(datadir2,"/",sample2,"_", binsize, "_segments.tsv"), col_types = c("fddd"),
   #                      col_names = c("chr", "start", "end", "ratio"), skip = 1)
   # 
   # segs_B <- segs_B %>% filter(!chr %in% c("X", "Y", "23", "24")) 
   
   tmp <- inner_join(tmp_A, tmp_B)
   tmp$abslog2diffPerBin <- abs(tmp$ratio_tumor - tmp$ratio_cfDNA)
   segdiff <- nrow(segs_B) - nrow(segs_A)
   
   meanDiffPerBin <- mean(tmp$abslog2diffPerBin*segdiff)
   cumulDiffPerBin <- sum(tmp$abslog2diffPerBin*segdiff)
   data.frame(cfDNA_ID = sample1, tumorDNA_ID = sample2, mean_abs_diff_log2 = meanDiffPerBin, cumulDiffPerBin = cumulDiffPerBin)
}

makeBinsComparison <- function(datadir1, datadir2, sample1, sample2, patientID){
   
   bins_top <- read_tsv(paste0(datadir1, str_subset(dir(datadir1, pattern = sample1), "bins_mask")), col_types = c("fdd?d"),
                        col_names = c("chr", "start", "end", "id", "ratio"), skip = 1)
   bins_top <- bins_top %>% filter(!chr %in% c("X", "Y", "23", "24"))%>% mutate(SampleID = sample1)
   
   if (tumor_input == "array_BE" | tumor_input == "array_CZ" | tumor_input == "WES_FR" ){
      bins_bottom <-  read_tsv(paste0(datadir2,"/",sample2,"_", binsize, ".tsv"), col_types = c("fddd"),
                               col_names = c("chr", "start", "end", "ratio"))
      bins_bottom <- bins_bottom %>% filter(!chr %in% c("X", "Y", "23", "24")) %>% mutate(SampleID = sample2)
      bins_bottom$chr <- factor(bins_bottom$chr, levels = chr_order)
      
   } else if (tumor_input == "sWGS"){
      bins_bottom <-  read_tsv(paste0(datadir2, str_subset(dir(datadir2, pattern = sample2), "bins_mask")), col_types = c("fdd?d"),
                               col_names = c("chr", "start", "end", "id", "ratio"))
      bins_bottom <- bins_bottom %>% filter(!chr %in% c("X", "Y", "23", "24"))%>% mutate(SampleID = sample2) %>% select(-id)
      bins_bottom$chr <- factor(bins_bottom$chr, levels = chr_order)
   }
   
   
   corplot_df_bins <- full_join(bins_top, bins_bottom, by = c("chr", "start", "end")) %>% select(-chr, -start, -end)
   colnames(corplot_df_bins) <- c("id", "L2R_cfDNA", "CFD_ID", "L2R_tumorDNA", "tumorDNA_ID")
   corplot_df_bins$PatientID <- patientID
   corplot_df_bins
   
}


if (!file.exists("./data/alldiffs.tsv")){
   alldiffs <- data.frame()
   L2Rcomparison <- data.frame()
   for (row in 1:nrow(sample_annotation)){
      sample1 <- sample_annotation[row,]$CFD_ID
      sample2 <- sample_annotation[row,]$tumorDNA_ID
      patientID <-  sample_annotation[row,]$PatientID
      tissueplatform <- sample_annotation[row,]$tumorDNA_assay_detail
      tumortype <- sample_annotation[row,]$TumorType
      datadir1 <- datadir_sWGS_cfDNA
      if (sample_annotation[row,]$tumorDNA_assay == "array_BE"){
         tumor_input = "array_BE"
         datadir2 <-  datadir_arrayBE
      } else if (sample_annotation[row,]$tumorDNA_assay == "array_CZ"){
         tumor_input = "array_CZ"
         datadir2 <-  datadir_arrayCZ
      } else if (sample_annotation[row,]$tumorDNA_assay == "WES_FR"){
         tumor_input = "WES_FR"
         datadir2 <-  datadir_WES
      } else if (sample_annotation[row,]$tumorDNA_assay == "sWGS"){
         tumor_input = "sWGS"
         datadir2 <-  datadir_sWGS_tissue
      }
      if (length(dir(datadir2, pattern = sample2) > 0  | length(dir(datadir1, pattern = sample1) > 0))) {
         tmp <- makeLog2comparison(datadir1, datadir2, sample1, sample2, patientID)
         tmp_L2R <- makeBinsComparison(datadir1, datadir2, sample1, sample2, patientID)
         alldiffs <- rbind(alldiffs, tmp)
         L2Rcomparison <- rbind(L2Rcomparison, tmp_L2R)
      }
      print(paste0("Processed: ", row, "/", nrow(sample_annotation)))
   }
   write_tsv(alldiffs, "./data/alldiffs.tsv")
   write_tsv(L2Rcomparison, "./data/L2R.tsv")
} else {
   alldiffs  <- read_tsv("./data/alldiffs.tsv")
   L2Rcomparison <- read_tsv("./data/L2R.tsv")
}

cfDNAvsTissue <- left_join(cfDNAvsTissue, alldiffs, by = c("CFD_ID" = "cfDNA_ID", "tumorDNA_ID" = "tumorDNA_ID"))
L2Rcomparison <- left_join(cfDNAvsTissue, L2Rcomparison, by = c("CFD_ID" = "CFD_ID", "tumorDNA_ID" = "tumorDNA_ID", "PatientID" = "PatientID"))
if (!file.exists("./data/merged_segments.tsv")){
   read_segments <- function(datadir){
      files<-list.files(c(datadir),recursive=TRUE)
      files<-files[grep("egments_per_[0-9]+kb_mask", files)]
      tmp_heatmap <- data.frame()
      for(i in 1:length(files)){
         name_sample <- gsub("_segments_per_[0-9]+kb_mask.tsv", "", files[i])
         print(name_sample)
         tmp <-  read_tsv(paste0(datadir,files[i]), col_types = c("fddd"),
                          col_names = c("chr", "start", "end", "ratio"), skip = 1)
         
         colnames(tmp)<- c("chr", "start", "end", "ratio")
         tmp <- tmp %>% filter(!chr %in% c("X", "Y", "23", "24"))
         tmp$SampleID <- name_sample
         tmp$meanLog2 <- get.cpa.modified2(tmp)
         if (nrow(tmp_heatmap) == 0){
            tmp_heatmap <- tmp
         } else {
            tmp_heatmap <- rbind(tmp_heatmap, tmp)
         }
      }
      return(tmp_heatmap)
   }
   df_heatmap_init <- data.frame()
   df_heatmap_init <- rbind(df_heatmap_init, read_segments(datadir_sWGS_cfDNA))
   df_heatmap_init <- rbind(df_heatmap_init, read_segments(datadir_WES))
   df_heatmap_init <- rbind(df_heatmap_init, read_segments(datadir_sWGS_tissue))
   df_heatmap_init <- rbind(df_heatmap_init, read_segments(datadir_arrayCZ))
   df_heatmap_init <- rbind(df_heatmap_init, read_segments(datadir_arrayBE))
   
   df_heatmap_healthy <- data.frame()
   df_heatmap_healthy <- rbind(df_heatmap_healthy, read_segments(datadir_healthy))
   write_tsv(df_heatmap_init, "./data/merged_segments.tsv")
   write_tsv(df_heatmap_healthy, "./data/merged_segments_healthy.tsv")
} else {
   df_heatmap_init <- read_tsv("./data/merged_segments.tsv")
   df_heatmap_healthy <- read_tsv("./data/merged_segments_healthy.tsv")
}

df_heatmap_init$SampleID <- gsub("_.*", "", df_heatmap_init$SampleID)
df_heatmap_init <- df_heatmap_init 

a <- df_heatmap_init %>% distinct(meanLog2, SampleID)
cfDNAvsTissue <- left_join(cfDNAvsTissue, a, by = c("CFD_ID" = "SampleID"))
cfDNAvsTissue <- left_join(cfDNAvsTissue, a, by = c("tumorDNA_ID" = "SampleID"))
cfDNAvsTissue$deltaLog2 <- abs(cfDNAvsTissue$meanLog2.x - cfDNAvsTissue$meanLog2.y)
a <- NULL

6 Difference plot between cfDNA and tumor DNA

tumor = "nephroblastoma"
makeBarTumorvscfDNA <- function(tumor){
   barPlot <- df_heatmap_init
   sampleFilt_a <- sample_annotation_long %>% filter(cfDNA_HMW_ratio > 5) %>% filter(TumorType == tumor & biomaterial == "cfDNA") %>% pull(SampleID)
   barPlot_a <- barPlot %>% filter(SampleID %in% sampleFilt_a)
   barPlot_a <- barPlot_a %>% group_by(chr, start) %>%
      summarise(mean = mean(ratio, na.rm = TRUE),
                sd = sd(ratio, na.rm = TRUE),
                n = n()) %>%
      mutate(se = sd / sqrt(n),
             lower.ci = mean - qt(1 - (0.05 / 2), n - 1) * se,
             upper.ci = mean + qt(1 - (0.05 / 2), n - 1) * se) 
   barPlot_a$biomaterial <- "cfDNA"
   
   sampleFilt_b <- sample_annotation_long %>% filter(cfDNA_HMW_ratio > 1) %>% filter(TumorType == tumor & biomaterial == "tumor DNA") %>% pull(SampleID)
   barPlot_b <- barPlot %>% filter(SampleID %in% sampleFilt_b)
   barPlot_b <- barPlot_b %>% group_by(chr, start) %>%
      summarise(mean = mean(ratio, na.rm = TRUE),
                sd = sd(ratio, na.rm = TRUE),
                n = n()) %>%
      mutate(se = sd / sqrt(n),
             lower.ci = mean - qt(1 - (0.05 / 2), n - 1) * se,
             upper.ci = mean + qt(1 - (0.05 / 2), n - 1) * se) 
   barPlot_b$biomaterial <- "tumor DNA"
   
   
   ggplot() + 
      theme_bw() + 
      # labs(title = paste0(patientID, " - ", tissueplatform, " tissue - ", sample2, " - ",cpa.calc, ": ", cpa_bottom, " - ", tumortype), y = "log2(ratio)") +
      geom_bar(data = barPlot_a, aes(x = start, y = mean, col = biomaterial, fill = biomaterial), stat = "identity", position = "dodge", alpha = 1, size = 0) +
      geom_bar(data = barPlot_b, aes(x = start, y = mean, col = biomaterial, fill = biomaterial), stat = "identity", position = "dodge", alpha = 0.5, size = 0) +
      lims(y = c(-.6,.6))+ 
      labs(title = paste0(tumor, " (n = ", length(sampleFilt_b),")"), y = "mean of log2(ratio) across all samples")+
      theme(panel.spacing.x = unit(0, "lines"),
            panel.spacing.y = unit(0, "lines"),
            axis.title.x =  element_blank(),
            axis.text.x =  element_blank(),
            axis.ticks.x =  element_blank(),
            strip.background = element_rect(color = "white", fill = "white"),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.background = element_blank()) + 
      geom_hline(yintercept = 0, linetype = "dashed", col = "gray") +
      facet_wrap(~chr, strip.position = "bottom", scales ="free_x", nrow = 1)}

p1 <- makeBarTumorvscfDNA("neuroblastoma")
p2 <- makeBarTumorvscfDNA("nephroblastoma")
p3 <- makeBarTumorvscfDNA("osteosarcoma")
p4 <- makeBarTumorvscfDNA("rhabdomyosarcoma")
p5 <- makeBarTumorvscfDNA("Ewing sarcoma")

7 Sequencing quality control metrics

7.2 Duplicate percentage

9 Comparison of cfDNA vs tissue

9.1 Crosstable of cfDNA and tumor CNAs

9.2 Correlation of CPAm and CPA

Where possible (shallow WGS samples, n = 82), the correlation between the CPA and CPAm was calculated and yielded a Pearson R of 0.74 and a spearman rho of 0.86. The CPAm threshold of copy number neutral (“normal” or “flat”) at the 1% false discovery level in cfDNA was calculated on the cohort of healthy controls (individuals above 18 years old with no cancer diagnosis in their past medical history) from Raman et al. and was found to be 0.354. With this threshold, there are 4/82 cfDNA samples that were labeled discordant, i.e., copy number neutral with CPA and copy number aberrations with CPAm. Upon manual inspection, 3 out of these 4 samples contain visible CNAs, while 1 out of 4 samples is copy number neutral.

The samples that were discordant between CPA (1% FDR = 1.5344485) and CPAm (1% FDR = 0.3549618) are:

9.5 CPA cfDNA vs Pearson R ~ cfDNA/HMW ratio

9.6 Tumor types in dataset

ggsave("./plots/tumorsInDataset.png", plot = ggplot2::last_plot(), dpi = 300, width = 10, height = 6)
ggsave("./plots/tumorsInDataset.pdf", plot = ggplot2::last_plot(), dpi = 300, width = 10, height = 6)

cfDNAvsTissue_select <- cfDNAvsTissue_withHealthy %>% filter(TumorType %in% c("neuroblastoma", "nephroblastoma", "osteosarcoma", "rhabdomyosarcoma", "Ewing sarcoma", "healthy") & (cpa_type == "CPAm")) %>% filter(as.numeric(cfDNA_HMW_ratio) >= 0)

ridgeplot_cfDNA_per_tumor <- ggplot(
   cfDNAvsTissue_select %>% filter(TumorType != "healthy"), 
   aes(y = TumorType, x = cpa_cfDNA, fill = TumorType)) +
   geom_density_ridges(show.legend = FALSE, scale = 0.8, panel_scaling = FALSE,
                       point_alpha = 0.7, alpha = .2, point_shape = 1,
                       aes(point_color = TumorType,
                           point_fill = TumorType,
                           point_size = as.numeric(cfDNA_HMW_ratio)), 
                       jittered_points = TRUE, position = "raincloud") +
   theme_bw() + 
   labs(point_size = "cfDNA/HMW ratio", fill = "tumor", point_fill = "tumor", point_color = "tumor", x = "CPAm cfDNA", y = "tumor") + 
   geom_boxplot(alpha = 0.4, width = 0.2, show.legend = FALSE, outlier.shape = NA) + 
   theme(legend.position="right")+
   geom_vline(data=cfDNAvsTissue %>% filter(cpa_type == "CPAm"), alpha = 0.6, aes(xintercept = FDR_CPAm), linetype = "dashed")+
   annotate("rect",xmin = 0, xmax = FDR_CPAm, ymin = -Inf, ymax = Inf, alpha=0.3, fill="grey")  + lims(x = c(0,NA))


ridgeplot_tissueDNA_per_tumor <- ggplot(
   cfDNAvsTissue_select %>% filter(TumorType != "healthy"), 
   aes(y = TumorType, x = cpa_tumorDNA, fill = TumorType)) +
   geom_density_ridges(show.legend = FALSE, scale = 0.8, panel_scaling = FALSE,
                       point_alpha = 0.7, alpha = .2, point_shape = 1,
                       aes(point_color = TumorType, point_fill = TumorType), jittered_points = TRUE, position = "raincloud") +
   theme_bw() + 
   labs(y="", point_size = "cfDNA/HMW ratio", fill = "tumor", point_fill = "tumor", point_color = "tumor", x = "CPAm tumorDNA", y = "tumor") + 
   geom_boxplot(alpha = 0.4, width = 0.2, show.legend = FALSE, outlier.shape = NA) + 
   theme(legend.position="right") + theme(axis.text.y = element_blank())

9.7 Impact on neuroblastoma risk stratification

9.7.1 All samples

cfDNA tumor Total
MYCN neutral MYCN gain
MYCN neutral 56
100 %
100 %
80 %
0
0 %
0 %
0 %
56
100 %
80 %
80 %
MYCN gain 0
0 %
0 %
0 %
14
100 %
100 %
20 %
14
100 %
20 %
20 %
Total 56
80 %
100 %
80 %
14
20 %
100 %
20 %
70
100 %
100 %
100 %
χ2=63.890 · df=1 · φ=1.000 · Fisher’s p=0.000

9.7.2 cfDNA/HMW ratio above 1

cfDNA tumor Total
MYCN neutral MYCN gain
MYCN neutral 30
100 %
100 %
73.2 %
0
0 %
0 %
0 %
30
100 %
73.2 %
73.2 %
MYCN gain 0
0 %
0 %
0 %
11
100 %
100 %
26.8 %
11
100 %
26.8 %
26.8 %
Total 30
73.2 %
100 %
73.2 %
11
26.8 %
100 %
26.8 %
41
100 %
100 %
100 %
χ2=36.064 · df=1 · φ=1.000 · Fisher’s p=0.000

9.8 Impact on 1q gain in nephroblastoma

9.8.1 All samples

cfDNA tumor Total
1q neutral 1q gain
1q neutral 14
73.7 %
87.5 %
58.3 %
5
26.3 %
62.5 %
20.8 %
19
100 %
79.2 %
79.1 %
1q gain 2
40 %
12.5 %
8.3 %
3
60 %
37.5 %
12.5 %
5
100 %
20.8 %
20.8 %
Total 16
66.7 %
100 %
66.7 %
8
33.3 %
100 %
33.3 %
24
100 %
100 %
100 %
χ2=0.789 · df=1 · φ=0.290 · Fisher’s p=0.289

9.8.2 cfDNA/HMW ratio above 1

cfDNA tumor Total
1q neutral 1q gain
1q neutral 10
71.4 %
83.3 %
55.6 %
4
28.6 %
66.7 %
22.2 %
14
100 %
77.8 %
77.8 %
1q gain 2
50 %
16.7 %
11.1 %
2
50 %
33.3 %
11.1 %
4
100 %
22.2 %
22.2 %
Total 12
66.7 %
100 %
66.7 %
6
33.3 %
100 %
33.3 %
18
100 %
100 %
100 %
χ2=0.040 · df=1 · φ=0.189 · Fisher’s p=0.569

10 Generalized Additive Modelling

10.1 Data cleaning

  • Number of total observations 1664725
  • NUmber of observations after removing all NAs: 1227545

10.3 Summary

## NULL

10.4 Visualisation of assumptions

## 
## Method: fREML   Optimizer: perf chol
## $grad
## [1] 0.00000006012634
## 
## $hess
##          [,1]
## [1,] 40.55676
## 
## Model rank =  103 / 103 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                k'  edf k-index p-value
## s(PatientID) 89.0 81.7      NA      NA

10.5 Regression coefficient table

  L2R_cfDNA
Predictors Estimates CI p
(Intercept) -0.0025 -0.0136 – 0.0086 0.656
cfDNA_HMW_ratio_log10 0.0035 -0.0006 – 0.0076 0.097
L2R_tumorDNA 0.0043 0.0009 – 0.0077 0.012
L2R_tumorDNA:cfDNA_HMW_ratio_log10 0.1461 0.1446 – 0.1477 <0.001
L2R_tumorDNA:metastaticTRUE 0.1561 0.1529 – 0.1594 <0.001
L2R_tumorDNA:TumorTypenephroblastoma 0.3218 0.3169 – 0.3267 <0.001
L2R_tumorDNA:TumorTypeneuroblastoma 0.2693 0.2650 – 0.2737 <0.001
L2R_tumorDNA:TumorTypeosteosarcoma 0.1377 0.1337 – 0.1417 <0.001
L2R_tumorDNA:TumorTyperhabdomyosarcoma 0.2619 0.2569 – 0.2669 <0.001
Ewingsarcoma Reference
nephroblastoma -0.0021 -0.0153 – 0.0111 0.752
neuroblastoma 0.0024 -0.0109 – 0.0156 0.726
osteosarcoma 0.0029 -0.0126 – 0.0185 0.713
rhabdomyosarcoma 0.0036 -0.0114 – 0.0185 0.638
FALSE Reference
TRUE 0.0029 -0.0057 – 0.0115 0.512
patient_001 Reference
patient_002 Reference
patient_003 Reference
patient_004 Reference
patient_005 Reference
patient_006 Reference
patient_007 Reference
patient_008 Reference
patient_009 Reference
patient_010 Reference
patient_011 Reference
patient_013 Reference
patient_014 Reference
patient_015 Reference
patient_016 Reference
patient_017 Reference
patient_022 Reference
patient_023 Reference
patient_025 Reference
patient_027 Reference
patient_028 Reference
patient_029 Reference
patient_031 Reference
patient_032 Reference
patient_033 Reference
patient_034 Reference
patient_035 Reference
patient_036 Reference
patient_037 Reference
patient_038 Reference
patient_039 Reference
patient_041 Reference
patient_043 Reference
patient_053 Reference
patient_054 Reference
patient_055 Reference
patient_056 Reference
patient_057 Reference
patient_058 Reference
patient_060 Reference
patient_072 Reference
patient_073 Reference
patient_074 Reference
patient_075 Reference
patient_077 Reference
patient_078 Reference
patient_079 Reference
patient_080 Reference
patient_082 Reference
patient_083 Reference
patient_084 Reference
patient_105 Reference
patient_108 Reference
patient_110 Reference
patient_111 Reference
patient_116 Reference
patient_117 Reference
patient_118 Reference
patient_121 Reference
patient_122 Reference
patient_127 Reference
patient_128 Reference
patient_131 Reference
patient_135 Reference
patient_136 Reference
patient_137 Reference
patient_138 Reference
patient_139 Reference
patient_146 Reference
patient_152 Reference
patient_159 Reference
patient_168 Reference
patient_170 Reference
patient_180 Reference
patient_183 Reference
patient_185 Reference
patient_186 Reference
patient_188 Reference
patient_189 Reference
patient_191 Reference
patient_192 Reference
patient_194 Reference
patient_202 Reference
patient_203 Reference
s(PatientID) 81.7364 <0.001
patient_204 Reference
patient_211 Reference
patient_212 Reference
patient_217 Reference
patient_219 Reference
Observations 1131209
R2 0.356

11 Heatmap

df_heatmap_init = df_heatmap_init %>% select(-c(meanLog2))

# params to test function with
#sample_select <- c("osteosarcoma", "Ewing sarcoma", "rhabdomyosarcoma")
#width_px = 1200
#height_px = 1800
#sample_select <- "neuroblastoma"

makeHeatmap <- function(df_heatmap_init, sample_select, width_px, height_px, arrangehm = "tumor"){
   #df_heatmap = df_heatmap_init %>% filter(str_detect(SampleID, neuroblastoma))
   df_heatmap <- df_heatmap_init
   df_heatmap$bin <- paste0(as.character(df_heatmap$chr), ":", as.character(df_heatmap$start), "-", as.character(df_heatmap$end))
   
   # find cause of duplicates
   df_heatmap <- df_heatmap %>% select(-c(chr, start, end)) %>% group_by(SampleID) %>% distinct(bin, .keep_all = TRUE) %>% ungroup() %>% spread(key = c(bin), value = ratio)
   df_heatmap <- df_heatmap %>% 
      select(where(~!any(is.na(.))))
   #df_heatmap <- df_heatmap %>% distinct(SampleID, .keep_all = TRUE)
   
   colnames_hm <- paste0(gsub(":.*", "", colnames(df_heatmap[,2:ncol(df_heatmap)])))
   colnames_hm <- factor(colnames_hm, levels = chr_order)
   rownames_hm <- df_heatmap$SampleID
   
   
   sampleTypes <- sample_annotation_long %>% filter(SampleID  %in% rownames_hm)
   
   sampleTypes <- sampleTypes %>% dplyr::arrange(PatientID)
   
   df_heatmap <- inner_join(sampleTypes, df_heatmap)
   if (arrangehm == "pearsonR"){
      df_heatmap <- df_heatmap %>% dplyr::arrange(desc(pearsonR),PatientID, TumorType)
   }else{
      df_heatmap <- df_heatmap %>% dplyr::arrange(PatientID, TumorType)
   }
   df_heatmap <- df_heatmap %>% distinct(SampleID, `1:100400001-100600000`, 
                                         `1:107200001-107400000`,
                                         `1:113000001-113200000`,
                                         `1:117800001-118000000`, .keep_all = TRUE)
   if (arrangehm == "pearsonR"){
      df_heatmap <- df_heatmap %>% filter(PatientID %in% sample_select)
   } else{
      df_heatmap <- df_heatmap %>% filter(TumorType %in% sample_select)
   }
   
   df_heatmap$pearsonR <- round(df_heatmap$pearsonR, 2)
   #df_heatmap$pearsonR <- ifelse(df_heatmap$biomaterial == "cfDNA", NA, df_heatmap$pearsonR)
   #    cbind(sample_annotation$sWGS, sample_annotation$TumorType, rep("sWGS", nrow(sample_annotation))),
   #    cbind(sample_annotation$array, sample_annotation$TumorType, rep("array", nrow(sample_annotation)))
   # ))
   # colnames(sampleTypes) <- c("SampleID", "tumor", "type")
   
   ht_opt(
      legend_title_gp = gpar(fontsize = 20, fontface = "bold"),
      legend_labels_gp = gpar(fontsize = 20),
      ROW_ANNO_PADDING = unit(8,"mm")
   )
   
   tumor_col <- colorRampPalette(colors = brewer.pal(12, "Paired")) (length(levels(as.factor(df_heatmap$TumorType))))
   names(tumor_col) <- levels(as.factor(df_heatmap$TumorType))
   
   platform_col <- colorRampPalette(colors = brewer.pal(9, "Set1")) (length(levels(as.factor(df_heatmap$assayDetail))))
   names(platform_col) <- levels(as.factor(df_heatmap$assayDetail))
   
   biomat_col <- colorRampPalette(colors = brewer.pal(3, "Set3")) (length(levels(as.factor(df_heatmap$biomaterial))))
   names(biomat_col) <- levels(as.factor(df_heatmap$biomaterial))
   
   source_col <- colorRampPalette(colors = brewer.pal(5, "Set2")) (length(levels(as.factor(df_heatmap$source))))
   names(source_col) <- levels(as.factor(df_heatmap$source))
   
   pearsonR_col <- colorRamp2(c(-0.1, 1), colors = c("white", "purple"))
   
   ha = HeatmapAnnotation(
      CPAm = anno_barplot(df_heatmap$CPAm, gp = gpar(fill = 2, col = 2)),
      pearsonR = df_heatmap$pearsonR,
      platform = df_heatmap$assayDetail,
      col = list(platform = platform_col, biomaterial = biomat_col, tumor = tumor_col, source = source_col),
      biomaterial = df_heatmap$biomaterial,
      tumor = df_heatmap$TumorType,
      source  = df_heatmap$source,
      annotation_name_side = "bottom", which = "row", show_legend = TRUE,
      width = unit(6, "cm"),
      gap = unit(0.5, "mm"),
      show_annotation_name = TRUE,
      simple_anno_size = unit(0.8, "cm"),
      annotation_legend_param = 
         list(
            platform = list(
               title = "platform"
            ),
            biomaterial = list(
               title = "biomaterial"
            ),
            tumor = list(
               title = "tumor", 
               ncol = 1
            ),
            source = list(
               title = "source", 
               ncol = 1
            )
         ))
   
   loss <- wes_palette("Zissou1")[1]
   gain <- wes_palette("Zissou1")[5]
   neutral <- "#F1F1F1"
   hm_q <- quantile(df_heatmap[,12:ncol(df_heatmap)], na.rm = TRUE, probs = c(0.01, 0.99))
   col_fun = colorRamp2(c(hm_q[1], 0, hm_q[2]), c(loss, neutral, gain))
   

   
   if (arrangehm == "pearsonR"){
      hm_df <- as.matrix(df_heatmap[,12:ncol(df_heatmap)])
      rownames(hm_df) <- as.factor(df_heatmap$pearsonR)
      arr <- order((rownames(hm_df)))
      split <- c(rep(1, nrow(hm_df)/2), rep(2, nrow(hm_df)/2))
      row_gap <- 5
      filename <- "_pearsonR_"
      hm_df <- hm_df[,2:ncol(hm_df)]
   } else {
      hm_df <- as.matrix(df_heatmap[,13:ncol(df_heatmap)])
      rownames(hm_df) <- as.factor(df_heatmap$pearsonR)
      arr <- NULL
      split <- df_heatmap$PatientID
      row_gap <- 0.4
      filename <- "_bytumor_"
   }
   ht <- Heatmap(hm_df, 
                 name = "log2ratio", column_split = colnames_hm, row_split = split,
                 col =   col_fun,
                 row_title = FALSE,
                 row_gap = unit(row_gap, "mm"),
                 cluster_columns = FALSE, 
                 cluster_rows = FALSE,
                 show_column_names = FALSE,
                 show_row_names = TRUE, 
                 row_labels = df_heatmap$PatientID, 
                 row_order = arr,
                 right_annotation = ha,
                 column_title_gp = gpar(fontsize = 10),
                 column_gap = unit(1, "mm"))
   png(paste0(plotfolder, "heatmap", filename, sample_select[1], ".png"), width = width_px, height = height_px, pointsize = 14 )
   map <- draw(ht, heatmap_legend_side = "bottom")
   dev.off()
   
}
library(data.table)

# to add middle 10
#pearson_vect <- c(sample_annotation_long %>% arrange(desc(pearsonR)) %>% head(n = 20) %>% pull(PatientID), setorder(data.table(sample_annotation_long), pearsonR)[(.N/2 - 20/2):(.N/2 + 20/2 - 1), ] %>% pull(PatientID), sample_annotation_long %>% arrange(desc(pearsonR)) %>% filter(!is.na(pearsonR)) %>% tail(n = 20) %>% pull(PatientID))

pearson_vect <- c(sample_annotation_long %>% filter(PatientID != "patient_101") %>% arrange(desc(pearsonR)) %>% head(n = 30) %>% pull(PatientID), sample_annotation_long %>% filter(PatientID != "patient_101") %>% arrange(desc(pearsonR)) %>% filter(!is.na(pearsonR)) %>% tail(n = 30) %>% pull(PatientID))

makeHeatmap(df_heatmap_init = df_heatmap_init, pearson_vect, width_px = 1200, height_px = 1400, arrangehm = "pearsonR")
## quartz_off_screen 
##                 2

11.1 Adrenal tumors

## quartz_off_screen 
##                 2
heatmap neuroblastoma and ganglioneuroblastoma

heatmap neuroblastoma and ganglioneuroblastoma

11.2 Sarcomas

## quartz_off_screen 
##                 2
heatmap rhabdomyosarcoma, Ewing sarcoma and rhabdomyosarcoma

heatmap rhabdomyosarcoma, Ewing sarcoma and rhabdomyosarcoma

12 Hetereogeneity plots

example heterogeneity plot before pictures of resection are added

example heterogeneity plot before pictures of resection are added

14 Result section in RMarkdown

The results section was written in Rmarkdown, the numbers were pulled immediately from the dataframes in this RMarkdown file. For the code, see the .Rmd file (as opposed to the .html file).

Sample collection. We retrospectively included 285 unique samples (n = 139 plasma, n = 4 CSF, n = 142 tumor tissue) of 140 unique pediatric cancer cases. Patients were recruited at Ghent University Hospital (n = 113), Princess Máxima Center (n = 7), Institut Curie (n = 5) and University Hospital Motol (n = 15). In total, the cohort comprised Ewing sarcoma (n = 9), osteosarcoma (n = 10), rhabdomyosarcoma (n = 11), nephroblastoma (n = 22), neuroblastoma (n = 70) and brain tumor samples (n = 12). More detailed sample information is summarized in supplementary table X. From these 140 patients, copy number aberrations (CNAs) were measured in plasma with shallow whole genome sequencing (sWGS) in all samples, while on tissue this was done either with sWGS (n = 70), WES (n = 5) or array CGH (n = 67). In case of sWGS, 18M [13520196.5-22303950] reads were generated for cfDNA and 39.79M [17697566.5-99577819] for tissue DNA, with 11.38% [8.4250943-15.26553] and 10.51% duplicate reads [6.5794449-17.9417297], respectively.

Copy number abnormality is higher in tumor tissue than in plasma. For every sample, the modified copy number profile abnormality (CPAm) score was calculated (see Methods for details, see figure ##2A and ##2B for an illustration of the relationship between the genome-wide copy number profile and the CPAm score). The median CPAm across all tumor types was found to be 0.796 [0.286075-1.771125] in cfDNA and 2.17315 [0.972925-4.033475] in tissue DNA (figure ###4B illustrates the CPAm score per tumor type). Based on manual inspection (tissue DNA) or the previously established 1% FDR threshold for CPAm (cfDNA, see Methods), we found that 60 (41.0958904%) cfDNA samples and 11 (7.5342466%) tumor samples were labeled as “flat”, i.e., copy number neutral.

cfDNA sample quality and disease extent determines concordance between cfDNA and tissue DNA. Previous studies have pointed at a substantial influence of cfDNA sample quality on the detection of tumor-derived DNA in cfDNA (ADD REFS). We assessed the cfDNA quality by determining the ratio of cfDNA (< 700 bp) vs. high molecular weight (> 700 bp) for 126 samples (3.3507869 [0.521626-15.075385], figure ##1 and supplemental figures #XX). For every cfDNA-tissue pair, the Pearson R and the CPAm score (see Methods) was calculated and associated with the cfDNA/HMW ratio (figure XX, supplemental figures XX). In Figure XX, we observed an apparent influence of cfDNA/HMW ratio and the tissue assay (i.e. Illumina BeadChip, sWGS, WES) on the copy number load in cfDNA. Subsequently, we more deeply investigated the effect of these parameters on the agreement between the tissue CNAs and cfDNA CNAs. Using a generalized additive model (GAM), we found that a higher cfDNA/HMW ratio (after log10 transformation) was associated with a better agreement between tissue CNA and cfDNA CNAs (p < 0.001), after adjusting for tumor type, disease extent and the platform on which the tissue copy number was determined (e.g. sWGS or Illumina BeadChip, full model in supplemental data X). Based on previously-defined thresholds (ref epigenetics), we found that of on a total of 126 samples, the 43 samples with a cfDNA/HMW ratio lower than 1 (low quality) contained 26 (60.4651163%) copy number neutral samples in cfDNA while of these 26 cfDNA neutral samples, there were 38 samples with CNAs in the tumor. Of 31 samples with a ratio between 1 and 5 (intermediate quality) 20 (64.516129%) were copy number neutral (all corresponding tumor samples contained CNAs). Finally, of 52 cfDNA samples with a ratio more than 5 (high quality), 3 (5.7692308%) were copy number neutral. Upon closer inspection of these three cases, the tumor was also copy number neutral in one (patient 008) and contained segmental aberrations in the other two cases (patient 044, patient 206). Overall, disagreement (i.e. tissue DNA containing CNAs while the plasma does not or vice versa) was seen in 1 samples and 50 samples, respectively. The cfDNA/HMW ratio in these 51 discordant samples is 1.11 [0.2558536-2.7754349], while the ratio in the concordant samples is 10.11 [0.9472801-21.7385013]. Furthermore, based on the GAM, patients with metastatic disease had a higher agreements between the log2(ratio) in cfDNA and log2(ratio) in tissue DNA.

Spatial heterogeneity in tumor samples. For two nephroblastoma cases, cfDNA was available at diagnosis and tumor tissue after treatment (patient XX and patient XX). Patient 56 and 57 were treated according to the SIOP Wilms tumor protocol for nephroblastoma (ADDS REFS). Plasma from these two patients was obtained before initiation of chemotherapy and tissue samples were obtained after 4 weeks of neoadjuvant chemotherapy. After resection, the copy number profile was determined in three different locations in the resected kidney and compared to the pre-treatment plasma sample (figure XXX2C), which revealed substantial intra-tumor difference and discordance with cfDNA (e.g. patient 56, gain on chr12, patient 57 both present in cfDNA but not in all tumor sections, figure 2C). For patient 56, histologic evaluation for locations 1 and 2 was determined to be triphasic nephroblastoma with necrosis and location 3 was kidney with blastema. For patient 57, locations 1 and 3 were determined to be triphasic nephroblastoma with rhabdomyoblastic differentiation and location 2 was necrotic tissue. Importantly, gain of 1q, a prognostic biomarker in Wilms tumor (https://pubmed.ncbi.nlm.nih.gov/27432915/), was only observed in location 1 of patient 56 and location 2 and 3 of patient 57, while not in cfDNA at diagnosis.

CNAs can be unique for cfDNA or for tissue DNA. Several samples, of moderate to high quality (cfDNA/HMW ratio above 1) and with a high CPAm (more than 3 times the CPAm at the 1% FDR threshold) in cfDNA were observed to have a low Pearson correlation coefficient with the respective tumor CPAm value (e.g. patient 079, patient 212, patient 077, patient 196). Upon closer inspection, several aberrations are discordant between plasma and tissue DNA (patient 077, patient 079, patient 196, figure XXX). Furthermore, other discordant samples were seen upon manual inspection, albeit with smaller and more subtle differences. In three neuroblastoma cases (patient 185, 136, 109) recurrent segmental CNAs (1p deletion, 2p gain, 11q deletion and 17q gain) were more clearly present in the cfDNA than in the tissue DNA. In one case (patient 212), only the amplification of MYCN on 2p24.3 could be detected in the tissue DNA while analysis of the cfDNA samples showed that many more CNAs were present (figure XXX). For several nephroblastoma cases (e.g. patient 035 and 056), chromosomal aberrations were identified in the cfDNA and not in the tissue DNA.

cfDNA is complementary to tissue DNA in the risk stratification of neuroblastoma and nephroblastoma. As MYCN amplification is an important prognostic biomarker in neuroblastoma, we investigated agreement between MYCN gain/amplification between cfDNA and tissue DNA. On all samples (irrespective of the sample quality) (n = 70), MYCN calls were similar in cfDNA and tissue DNA without any discrepancies. In nephroblastoma, relying on cfDNA for determining the presence of 1q gain, a prognostic marker (ADD REF), 5 samples (n = 24) (or 4/18 when only including the intermediate to high quality samples with a cfDNA/HMW ratio above 1) with 1q gain would have been missed, while only relying on tissue DNA 1q gain would have been missed in 2 cases.

Cerebrospinal fluid is preferable to plasma for medulloblastoma For brain tumors, it is expected that higher tissue DNA fractions will be found in cerebrospinal fluid (CSF) when compared to plasma. We analyzed 3 CSF samples of brain tumor patients. All showed clear CNAs in the CSF (almost) fully concordant to the matching tissue DNA profile (HEATMAP FIGURE). For patient XXX with a medulloblastoma, we had a matching plasma sample available, with a cfDNA/HMW ratio of 1.94, , with a cfDNA/HMW ratio of 1.9498525 that presented a copy number neutral profile, while CSF depicted a chromosome 6 loss (supplemental figure XX).

---
title: "The feasibility of using liquid biopsies as a complementary assay for copy number aberration profiling in routinely collected pediatric cancer patient samples "
subtitle: "Data analysis"
author: "Ruben Van Paemel"
date: "`r format(Sys.time(), '%d %B, %Y, %H:%M:%S')`"
output:
   html_document:
      code_download: yes
      code_folding: hide
      df_print: paged
      fig_caption: yes
      highlight: kate
      number_sections: yes
      theme: cosmo
      toc: yes
      toc_float: yes
---

<style>
body {
text-align: justify}
</style>


# Introduction
In our dataset, we have different datatypes:
- Whole genome sequencing at shallow depth (sWGS)
- Agilent 180K arrays
- Illumina HumanCytoSNP data

These needs to be harmonised first so that they can be read into R.

## sWGS
The fastq files were preprocessed with `./code/preprocessing/sWGS_pipeline_SE.snakefile`, which runs bwa-mem, picard markduplicates and WisecondorX/ichorCNA. The output (`bins.bed`, `segments.bed`) from WisecondorX were further used in the downstream processing. The reference for WisecondorX was generated from an in-house dataset of healthy volunteers. More information on how to obtain a reference dataset for WisecondorX is available at https://github.com/CenterForMedicalGeneticsGhent/WisecondorX. The PoN and supporting files for ichorCNA were obtained from the ichorCNA repository (https://github.com/broadinstitute/ichorCNA).

## WES
The fastq files were preprocessed with `./code/preprocessing/sWGS_pipeline_PE.snakefile`, which runs bwa-mem, picard markduplicates and WisecondorX/ichorCNA. 

CNVkit was run with the parameters described in `cnvkit.sh` on the deduplicated bam files with the paired germline sample. Target region bed files (e.g. SureSelect) was downloaded from the Agilent website. The link between the ID from germline WES and ID from tumor WES is also in the `cnvkit.sh` file. 

## Illumina HumanCytoSNP
Illumina Genomestudio 2.0 (https://www.illumina.com/techniques/microarrays/array-data-analysis-experimental-design/genomestudio.html) was used to obtain the log2ratio (Robs/Rexp) per bin from the IDAT files. The SampleSheets are available in `./resources/`. The other files (`.egt` and `.bpm` are too large to host here and are available on the Illumina website (e.g. https://emea.support.illumina.com/downloads/humancytosnp-12v2-1_product_files-ns.html)

## Agilent 180K array
Raw data for the Agilent 180K arrays was not available. The processed data (`sample_raw.csv`) was used for downstream processing. The data was processed according to the methods in https://pubmed.ncbi.nlm.nih.gov/23308108/. 

## Integrating the different platforms
`./code/convertRaw.snakefile`: converts data from obtained from WES, sWGS, Illumina HumanCytoSNP-12 or 180K array (Agilent) into a structure that allows the comparison of these different data-types, and harmonises the bin size between all data types. Also uses DNAcopy to obtain segmentations. If necessary (i.e. array data), the files are liftover to GRCh38.

Example input files are present in the `./examples/` folder. 

## Other scripts
`./code/makeBins.sh` obtain bins of choice starting from `.fa.fai` file, is a dependency of `./code/convertRaw.snakefile`

`./code/DNAcopy_segment.R`: runs DNAcopy, is a dependency of `./code/convertRaw.snakefile`

## Load dependencies and packages
```{r, warning=FALSE, message=FALSE}
knitr::opts_chunk$set(warning=FALSE, message=FALSE, cache = TRUE)
library(tidyverse)
library(wesanderson)
library(ggpubr)
library(zoo)
library(readxl)
library(reshape2)
library(ComplexHeatmap)
library(queensgambit) #https://github.com/rmvpaeme/queensgambit
library(circlize)
library(RColorBrewer)
library(ggbeeswarm)
library(ggridges)
library(conflicted)
library(sjPlot)
library(ggrepel)
library(DT)
conflict_prefer("filter", "dplyr")

options(scipen=999)
colortheme <- c(wes_palette("Cavalcanti1")[1], wes_palette("Cavalcanti1")[4])

chr_order <- c("1", "2", "3", "4", "5", "6", "7", "8",
               "9", "10", "11", "12", "13", "14", "15",
               "16", "17", "18", "19", "20", "21", "22", "X", "Y")

datadir_sWGS_cfDNA <- "./data/sWGS_cfDNA/"
datadir_sWGS_tissue <- "./data/sWGS_tissue/"
datadir_arrayBE <- "./data/array/"
datadir_arrayCZ <- "./data/SNParray/"
datadir_WES <- "./data/WES_FR/"
datadir_healthy <- "./data/sWGS_healthy/"
plotfolder <- "./plots/"
```

```{r}
# the original CPA from Raman et al.
get.cpa <- function(seg){
   cpa <- sum(abs(seg$zscore) * (seg$end - seg$start  + 1)) / nrow(seg) * 1e-8 # per 100 Mb
   return(cpa)
}

# The modified CPA score (CPAm)
get.cpa.modified2 <- function(seg){
   cpa <- sum(abs(seg$ratio) * (seg$end - seg$start + 1)) / 1e8
   return(cpa)
}

makeCNVcomparison <- function(datadir1, datadir2, sample1, sample2, patientID, uniqueID, binsize, tumor_input, tumortype, tissueplatform){
   
   print(paste0("cfDNA sample: ", sample1))
   print(paste0("tumorDNA sample: ", sample2))
   print(paste0("binsize: ", binsize))
   
   
   bins_top <- read_tsv(paste0(datadir1, str_subset(dir(datadir1, pattern = sample1), "bins_mask")), col_types = c("fdd?d"),
                        col_names = c("chr", "start", "end", "id", "ratio"), skip = 1)
   bins_top <- bins_top %>% filter(!chr %in% c("X", "Y", "23", "24"))
   
   segs_top <- read_tsv(paste0(datadir1, str_subset(dir(datadir1, pattern = sample1), "segments_mask.bed")), col_types = c("fdddd"),
                        col_names = c("chr", "start", "end", "ratio", "zscore"))
   segs_top <- segs_top %>% filter(!chr %in% c("X", "Y", "23", "24")) %>% mutate(Sample = sample1)
   
   if (tumor_input == "array_BE" | tumor_input == "array_CZ" | tumor_input == "WES_FR" ){
      bins_bottom <-  read_tsv(paste0(datadir2,"/",sample2,"_", binsize, ".tsv"), col_types = c("fddd"),
                               col_names = c("chr", "start", "end", "ratio"))
      bins_bottom <- bins_bottom %>% filter(!chr %in% c("X", "Y", "23", "24"))
      bins_bottom$chr <- factor(bins_bottom$chr, levels = chr_order)
      
      segs_bottom <- read_tsv(paste0(datadir2,"/",sample2,"_", binsize, "_segments.tsv"), col_types = c("fddd"),
                              col_names = c("chr", "start", "end", "ratio"), skip = 1)
      
      segs_bottom <- segs_bottom %>% filter(!chr %in% c("X", "Y", "23", "24")) %>% mutate(Sample = sample2)
      segs_bottom$chr <- factor(segs_bottom$chr, levels = chr_order)
   } else if (tumor_input == "sWGS"){
      bins_bottom <-  read_tsv(paste0(datadir2, str_subset(dir(datadir2, pattern = sample2), "bins_mask")), col_types = c("fdd?d"),
                               col_names = c("chr", "start", "end", "id", "ratio"))
      bins_bottom <- bins_bottom %>% filter(!chr %in% c("X", "Y", "23", "24"))
      bins_bottom$chr <- factor(bins_bottom$chr, levels = chr_order)
      
      segs_bottom <- read_tsv(paste0(datadir2, str_subset(dir(datadir2, pattern = sample2), "segments_mask.bed")), col_types = c("fdddd"),
                              col_names = c("chr", "start", "end", "ratio", "zscore"))
      segs_bottom <- segs_bottom %>% filter(!chr %in% c("X", "Y", "23", "24")) %>% mutate(Sample = sample2)
      segs_bottom$chr <- factor(segs_bottom$chr, levels = chr_order)
   }
   
   color_top <- wes_palette("Cavalcanti1")[1]
   color_bottom <- wes_palette("Cavalcanti1")[4]
   color_abberations <- wes_palette("Cavalcanti1")[5]
   
   max.ratio_bins <- max(c(bins_top$ratio, bins_bottom$ratio), na.rm = TRUE)
   min.ratio_bins <- min(c(bins_top$ratio, bins_bottom$ratio), na.rm = TRUE)
   
   max.ratio_bins.top <- max(c(bins_top$ratio), na.rm = TRUE)
   min.ratio_bins.top <- min(c(bins_top$ratio), na.rm = TRUE)
   
   max.ratio_bins.bottom <- max(c(bins_bottom$ratio), na.rm = TRUE)
   min.ratio_bins.bottom <- min(c(bins_bottom$ratio), na.rm = TRUE)
   
   
   if (tumor_input == "sWGS"){
      cpa_top <- round(get.cpa(segs_top),4)
      cpa_bottom <- round(get.cpa(segs_bottom),4)
      cpa_top <- round(get.cpa.modified2(segs_top),4)
      cpa_bottom <- round(get.cpa.modified2(segs_bottom),4)
      cpa.calc <- "CPAm"
   } else{
      cpa_top <- round(get.cpa.modified2(segs_top),4)
      cpa_bottom <- round(get.cpa.modified2(segs_bottom),4)
      cpa.calc <- "CPAm"
   }
   
   if (tumortype == "neuroblastoma"){
      # add MYCN region
      nbl_1 <- geom_segment(data = segs_top %>% filter(chr == "2"), aes(x=15940550, xend=15947004, y=-Inf, yend=Inf), col = "grey", alpha = 0.4) 
      nbl_2 <- geom_text(data = segs_top %>% filter(chr == "2"), label = "MYCN", x=65947004, y=max.ratio_bins*0.9, angle = 90, alpha = 0.4, size = 3, color = "grey")
   } else {
      nbl_1 <- NULL
      nbl_2 <- NULL
   }
   
   ptop <- ggplot(bins_top, aes(x = start, y = ratio)) + 
      theme_bw() + 
      labs(title = paste0(patientID, " - sWGS cfDNA - ", sample1, " - ", cpa.calc,": ", cpa_top, " - ", tumortype), y = "log2(ratio)") + 
      geom_point(size = 0.3, col = color_top, alpha = 0.7) +
      lims(y = c(min.ratio_bins,max.ratio_bins))+
      theme(panel.spacing.x = unit(0, "lines"),
            panel.spacing.y = unit(0, "lines"),
            axis.title.x =  element_blank(),
            axis.text.x =  element_blank(),
            axis.ticks.x =  element_blank(),
            strip.background = element_rect(color = "white", fill = "white"),
            #   panel.border = element_rect(color = "gray", fill = NA, size = 0.3), 
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.background = element_blank()) + 
      geom_hline(yintercept = 0, linetype = "dashed", col = "gray") +
      facet_wrap(~chr, strip.position = "bottom", scales ="free_x", nrow = 1) + 
      geom_segment(data = segs_top, aes(x = start, xend = end, y = ratio , yend = ratio), col = color_abberations) +
      geom_rect(data = segs_top, aes(xmin=start, xmax=end, ymin=0, ymax=ratio), fill = color_abberations, alpha = 0.4) + nbl_1 + nbl_2
   
   
   pbottom <- ggplot(bins_bottom, aes(x = start, y = ratio)) + 
      theme_bw() + 
      labs(title = paste0(patientID, " - ", tissueplatform, " tissue - ", sample2, " - ",cpa.calc, ": ", cpa_bottom, " - ", tumortype), y = "log2(ratio)") +  
      geom_point(size = 0.3, col = color_bottom, alpha = 0.7) +
      lims(y = c(min.ratio_bins,max.ratio_bins))+
      theme(panel.spacing.x = unit(0, "lines"),
            panel.spacing.y = unit(0, "lines"),
            axis.title.x =  element_blank(),
            axis.text.x =  element_blank(),
            axis.ticks.x =  element_blank(),
            strip.background = element_rect(color = "white", fill = "white"),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.background = element_blank()) + 
      geom_hline(yintercept = 0, linetype = "dashed", col = "gray") +
      facet_wrap(~chr, strip.position = "bottom", scales ="free_x", nrow = 1) +
      geom_segment(data = segs_bottom, aes(x = start, xend = end, y = ratio , yend = ratio), col = color_abberations) +
      geom_rect(data = segs_bottom, aes(xmin=start, xmax=end, ymin=0, ymax=ratio), fill = color_abberations, alpha = 0.4) + nbl_1 + nbl_2
   
   
   corplot_df_bins <- inner_join(bins_top, bins_bottom, by = c("chr", "start", "end"))
   #corplot_df <- corplot_df %>% filter(!is.na(ratio.x) & !is.na(ratio.y))
   
   corplot_df_bins <- corplot_df_bins %>%
      mutate(rM.top=rollapply(ratio.x,100, FUN=function(x) mean(x, na.rm=TRUE), fill=NA, align="right")) %>%
      mutate(rM.bottom=rollapply(ratio.y,100, FUN=function(x) mean(x, na.rm=TRUE), fill=NA, align="right")) 
   
   rollmean <- ggplot(tibble(corplot_df_bins), aes(x = start)) + 
      theme_bw() + 
      labs(y = "log2(ratio)", x = "chromosomes") +
      geom_line(aes(y=rM.bottom), col = color_bottom, alpha = 1, size = 1) +
      geom_line(aes(y=rM.top), col = color_top, alpha = 0.7, size = 1) +
      theme(panel.spacing.x = unit(0, "lines"),
            panel.spacing.y = unit(0, "lines"),
            #  axis.title.x =  element_blank(),
            axis.text.x =  element_blank(),
            axis.ticks.x =  element_blank(),
            strip.background = element_rect(color = "white", fill = "white"),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.background = element_blank()) + 
      geom_hline(yintercept = 0, linetype = "dashed", col = "gray") +
      facet_wrap(~chr, strip.position = "bottom", scales ="free_x", nrow = 1) + lims(y = c(-NA,NA))+ nbl_1
   
   max.ratio <- max(c(corplot_df_bins$ratio.x, corplot_df_bins$ratio.y), na.rm = TRUE)
   min.ratio <- min(c(corplot_df_bins$ratio.x, corplot_df_bins$ratio.y), na.rm = TRUE)
   corplot <- ggplot(corplot_df_bins, aes(x = ratio.x, y = ratio.y)) + 
      geom_point(size = 0.3, col = wes_palette("Royal1")[1]) + 
      theme_bw() +
      geom_abline(slope = 1, intercept = 0) +
      stat_cor(method = "pearson", aes(label = ..r.label..)) +
      geom_smooth(method = "lm", col = color_abberations, linetype = "dashed") +
      lims(x = c(min.ratio , max.ratio), y = c(min.ratio, max.ratio)) + 
      labs(x = "log2(ratio) cfDNA", y = "log2(ratio) tissue") +
      theme(axis.title.x = element_text(color = color_top),
            axis.title.y = element_text(color = color_bottom))
   
   R <- cor(corplot_df_bins$ratio.x, corplot_df_bins$ratio.y, use = "complete.obs", method = "pearson")
   
   CNVplot <- ggarrange(ptop, pbottom, nrow = 2, ncol = 1, align = "v", heights = c(1, 1))
   comparisons <- ggarrange(rollmean, corplot, nrow = 1, ncol = 2, widths = c(0.7, 0.3))
   
   arrangeplot <- ggarrange(CNVplot, comparisons, nrow = 2, heights = c(0.75, 0.25), widths = c(1, 0.90), align = "hv")
   ggsave(paste0(plotfolder, patientID, "_", sample1, "_", sample2, "_", binsize, ".png"), plot = arrangeplot, width = 11, height = 9, dpi = 300)
   
   df_comparison <- data.frame(UniqueID = c(uniqueID), PatientID = c(patientID), CFD_ID = c(sample1), tumorDNA_ID = c(sample2), pearsonR = c(R), cpa_cfDNA = c(cpa_top), cpa_tumorDNA = c(cpa_bottom), cpa_type = c(cpa.calc))
   return(df_comparison)
}
```

# Sample annotation
The sample annotation is read from the `xlsx file`.

```{r}
sample_annotation <- read_excel("/Users/rmvpaeme/Dropbox (speleman lab)/Basecamp/CVP/LQB pediatric patients/analysis_RVP/sample_annotation.xlsx")
sample_annotation <- sample_annotation %>% filter(cfDNA_data_available == "TRUE" & tumorDNA_data_available == "TRUE")
sample_annotation_full <- sample_annotation
sample_annotation <- sample_annotation %>% filter(is.na(filtersample))

sample_annotation$TumorGroup <- ifelse(
   sample_annotation$TumorType %in% c("Ewing sarcoma", "osteosarcoma"),
   "bone tumor",
   ifelse(
      sample_annotation$TumorType %in% c(
         "nephroblastoma",
         "malignant rhabdoid tumor of the kidney",
         "renal cell carcinoma",
         "kidney sarcoma"
      ),
      "kidney tumor",
      ifelse(
         sample_annotation$TumorType %in% c("neuroblastoma", "ganglioneuroblastoma"),
         "neuroblastoma",
         ifelse(
            sample_annotation$TumorType %in% c("rhabdomyosarcoma"),
            "rhabdomyosarcoma", "brain tumor"
         )
      )
   )
)


sample_annotation$TumorAbbrev <- sample_annotation$TumorType
sample_annotation$TumorAbbrev  <- gsub("Ewing sarcoma", "EWS", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("osteosarcoma", "OS", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("astrocytic pilocytoma", "ASP", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("ependymoma", "EPA", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("ganglioglioma", "GGA", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("ganglioneuroblastoma", "GNA", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("glioblastoma", "GBA", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("hemangioblastoma", "HGA", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("kidney sarcoma", "KS", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("meningioma", "MGA", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("malignant rhabdoid tumor of the kidney", "MRT", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("nephroblastoma", "NFB", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("neuroblastoma", "NBL", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("renal cell carcinoma", "RCC", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("rhabdomyosarcoma", "RMS", sample_annotation$TumorAbbrev)
sample_annotation$TumorAbbrev  <- gsub("medulloblastoma", "MBL", sample_annotation$TumorAbbrev)

sample_annotation$cfDNA_HMW_ratio <- as.numeric(sample_annotation$cfDNA_prct_conc)/as.numeric(sample_annotation$HMW_prct_conc)
sample_annotation$cfDNA_HMW_ratio <- as.numeric(sample_annotation$cfDNA_HMW_ratio)


sample_annotation$quality_score <- ifelse(sample_annotation$cfDNA_HMW_ratio < 1, "low",
                                          ifelse(sample_annotation$cfDNA_HMW_ratio < 5 & sample_annotation$cfDNA_HMW_ratio > 1, "medium", "high"))

sample_annotation$cfDNA_prct <- as.numeric(sample_annotation$cfDNA_prct_conc)/(as.numeric(sample_annotation$HMW_prct_conc)+as.numeric(sample_annotation$cfDNA_prct_conc))
sample_annotation$cfDNA_HMW_ratio <- as.numeric(sample_annotation$cfDNA_HMW_ratio)


tumorSamples <- sample_annotation %>% select(UniqueID, PatientID, tumorDNA_ID, TumorType,tumorDNA_assay,tumorDNA_assay_detail, tumorDNA_biomaterial, TumorGroup,cfDNA_HMW_ratio )
colnames(tumorSamples) <- c("UniqueID", "PatientID",  "SampleID", "TumorType", "assay", "assayDetail", "source", "TumorGroup", "cfDNA_HMW_ratio")
tumorSamples$biomaterial <- "tumor DNA"

cfDNASamples <-  sample_annotation %>% select(UniqueID, PatientID, CFD_ID, TumorType,tumorDNA_assay,tumorDNA_assay_detail, CFD_biomaterial, TumorGroup, cfDNA_HMW_ratio) 
colnames(cfDNASamples) <- c("UniqueID", "PatientID",  "SampleID", "TumorType", "assay", "assayDetail", "source", "TumorGroup", "cfDNA_HMW_ratio")
cfDNASamples$assay <- "sWGS"
cfDNASamples$assayDetail <- "sWGS"
cfDNASamples$biomaterial <- "cfDNA"

sample_annotation_long <- bind_rows(tumorSamples, cfDNASamples)
```

# Femto PULSE figures
```{r}
df_a <- read_csv("./data/FEMTO/2019 03 06 18H 06M Electropherogram.csv")
df_a$sample <- "cfDNA/HMW ratio [1,5) = 4.00"
#df_a$sample <- "CFD1806872"
colnames(df_a) <- c("Size (bp)", "RFU", "sample")


df_b <- read_csv("./data/FEMTO/2019 03 06 22H 35M Electropherogram.csv")
#df_b$sample <- "CFD1601677"
df_b$sample <- "cfDNA/HMW ratio [5,∞) = 37.50"
colnames(df_b) <- c("Size (bp)", "RFU", "sample")

df_c <- read_csv("./data/FEMTO/2019 03 06 23H 42M Electropherogram.csv")
#df_c$sample <- "CFD1600800"
df_c$sample <- "cfDNA/HMW ratio [0,1) = 0.78"
colnames(df_c) <- c("Size (bp)", "RFU", "sample")

df_size <- rbind(df_a, df_b, df_c)
ggplot(df_size, aes(y = `RFU`, x = `Size (bp)`, col = sample)) + geom_line() + scale_x_continuous(trans = "log10", breaks = c(100, 250, 700, 2500, 6000), limits = c(70,12000)) + theme_bw() + facet_wrap(~sample, scales = "free_y") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) + geom_vline(xintercept = 700, linetype = "dashed", color = "grey") + guides(col = FALSE) + scale_color_manual(values = qg_palette("USopen")[1:3])
ggsave("./plots/FP_ratios.pdf", plot = ggplot2::last_plot(), dpi = 300, width = 8, height = 4)
ggsave("./plots/FP_ratios.png", plot = ggplot2::last_plot(), dpi = 300, width = 8, height = 4)
ggsave("./plots/FP_ratios.svg", plot = ggplot2::last_plot(), dpi = 300, width = 8, height = 4)
```


# Reading in all data
While reading in the data, the loop makes a comparative figure for every comparison in sample_annotation, for example:

![comparison](/Users/rmvpaeme/Repos/RVPCVP2012_sWGS/plots/patient_039_CFD1802282_DNA044085_200kb.png)

```{r}

if (!file.exists("./data/compareAll.tsv")){
   cfDNAvsTissue <- data.frame()
   #sample_annotation <- sample_annotation  %>% filter(SampleOrigin == "MH")
   for (row in 1:nrow(sample_annotation)){
      sample1 <- sample_annotation[row,]$CFD_ID
      sample2 <- sample_annotation[row,]$tumorDNA_ID
      patientID <-  sample_annotation[row,]$PatientID
      uniqueID <-  sample_annotation[row,]$UniqueID
      tissueplatform <- sample_annotation[row,]$tumorDNA_assay_detail
      tumortype <- sample_annotation[row,]$TumorType
      datadir1 <- datadir_sWGS_cfDNA
      if (sample_annotation[row,]$tumorDNA_assay == "array_BE"){
         input_tumor = "array_BE"
         datadir2 <-  datadir_arrayBE
      } else if (sample_annotation[row,]$tumorDNA_assay == "array_CZ"){
         input_tumor = "array_CZ"
         datadir2 <-  datadir_arrayCZ
      } else if (sample_annotation[row,]$tumorDNA_assay == "WES_FR"){
         input_tumor = "WES_FR"
         datadir2 <-  datadir_WES
      } else if (sample_annotation[row,]$tumorDNA_assay == "sWGS"){
         input_tumor = "sWGS"
         datadir2 <-  datadir_sWGS_tissue
      }
      if (length(dir(datadir2, pattern = sample2) > 0  | length(dir(datadir1, pattern = sample1) > 0))) {
         tmp <- makeCNVcomparison(datadir1, datadir2, sample1, sample2, patientID, uniqueID, "200kb", input_tumor, tumortype,tissueplatform)
         cfDNAvsTissue <- rbind(cfDNAvsTissue, tmp)
      }
      print(paste0("Processed: ", row, "/", nrow(sample_annotation)))
   }
   write_tsv(cfDNAvsTissue, "./data/compareAll.tsv")
} else {
   cfDNAvsTissue  <- read_tsv("./data/compareAll.tsv")
}

a <- cfDNAvsTissue %>% select(PatientID,  UniqueID, CFD_ID, cpa_cfDNA, pearsonR)
colnames(a) <- c("PatientID", "UniqueID","SampleID", "CPAm", "pearsonR")
a$biomaterial <- "cfDNA"

b <- cfDNAvsTissue %>% select(PatientID, UniqueID, tumorDNA_ID, cpa_tumorDNA, pearsonR )
colnames(b) <-  c("PatientID", "UniqueID","SampleID", "CPAm", "pearsonR")
b$biomaterial <- "tumor DNA"

a <- rbind(a,b)
sample_annotation_long <- left_join(sample_annotation_long, a)
a <- NULL
b <- NULL


cfDNAvsTissue <- merge(sample_annotation, cfDNAvsTissue, by = c("PatientID", "UniqueID", "PatientID", "CFD_ID", "tumorDNA_ID"))


normal_CPA <- read_csv("./data/sWGS_healthy/CPA_all.csv") %>% dplyr::select(-CPAm) %>% melt() 
colnames(normal_CPA) <- c("CFD_ID","cpa_type", "cpa_cfDNA")

normal_CPAm <- read_csv("./data/sWGS_healthy/CPA_all.csv") %>% dplyr::select(-CPA) %>% melt() 
colnames(normal_CPAm) <- c("CFD_ID","cpa_type", "cpa_cfDNA")
normal_CPAm$cpa_tumorDNA <- normal_CPA$cpa_cfDNA
normal_CPAm$TumorType <- "healthy"
normal_CPAm$CFD_biomaterial <- "plasma"
normal_CPAm$SampleOrigin <- "UZG"
normal_CPAm$cfDNA_HMW_ratio <- 0
cfDNAvsTissue_withHealthy <- bind_rows(cfDNAvsTissue, normal_CPAm)

```

# Calculation of the 1% FDR for CPAm/CPA values
A normal distribution is fitted to the CPAm values, and based on the mean and SD, the qnorm(.99) is determined to obtain the 1% FDR ratio.

```{r}
calculate_CPA_FDR <- function(input, title ){
   P = ecdf(input %>% pull(cpa_cfDNA)) 
   plot(P, main = title)
   
   library(MASS)
   x <- input %>% pull(cpa_cfDNA)
   fit_normal <- fitdistr(x, densfun ="normal")
   para <- fit_normal$estimate
   
   curve(dnorm(x, para[1], para[2]), col = 2, add = FALSE, xlim = c(0,1.5))
   hist(input %>% pull(cpa_cfDNA), prob=TRUE, add = TRUE)
   
   
   FDR_CPA <- qnorm(.99, mean = para[1], sd = para[2])
   detach("package:MASS", unload = TRUE)
   return(FDR_CPA)
}

FDR_CPAm <- calculate_CPA_FDR(normal_CPAm, title = "cumulative plot CPAm")
FDR_CPA <- calculate_CPA_FDR(normal_CPA, title = "cumulative plot CPA ")

#cfDNAvsTissue$CNA_tumor <- ifelse(cfDNAvsTissue$cpa_tumorDNA < FDR_CPAm, "flat", "CNA")
cfDNAvsTissue$CNA_cfDNA <- ifelse(cfDNAvsTissue$cpa_cfDNA < FDR_CPAm, "flat", "CNA")
#cfDNAvsTissue$CNA_all <- paste0(cfDNAvsTissue$CNA_tumor, "-", cfDNAvsTissue$CNA_cfDNA)
```

The 1% FDR for CPAm is `r FDR_CPAm` and the 1% FDR for CPA is `r FDR_CPA`. 

```{r}
library(geiger)
binsize = "200kb"

makeLog2comparison <- function(datadir1, datadir2, sample1, sample2, patientID){
   
   print(paste0("cfDNA sample: ", sample1))
   print(paste0("tumorDNA sample: ", sample2))
   print(paste0("binsize: ", binsize))
   
   
   tmp_A <- read_tsv(paste0(datadir1, str_subset(dir(datadir1, pattern = sample1), "segments_per_200kb_mask.tsv")), col_types = c("fddd"),
                     col_names = c("chr", "start", "end", "ratio_cfDNA"), skip = 1)
   tmp_A <- tmp_A %>% filter(!chr %in% c("X", "Y", "23", "24")) 
   
   
   segs_A <- read_tsv(paste0(datadir1, str_subset(dir(datadir1, pattern = sample1), "segments_mask.bed")), col_types = c("fdddd"),
                      col_names = c("chr", "start", "end", "ratio", "zscore"))
   segs_A <- segs_A %>% filter(!chr %in% c("X", "Y", "23", "24"))
   
   tmp_B <- read_tsv(paste0(datadir2, str_subset(dir(datadir2, pattern = sample2), "segments_per_200kb_mask.tsv")), col_types = c("fddd"),
                     col_names = c("chr", "start", "end", "ratio_tumor"), skip = 1)
   tmp_B <- tmp_B %>% filter(!chr %in% c("X", "Y", "23", "24")) 
   
   if (tumor_input == "array_BE" | tumor_input == "array_CZ" | tumor_input == "WES_FR"){
      segs_B <- read_tsv(paste0(datadir2,"/",sample2,"_", binsize, "_segments.tsv"), col_types = c("fddd"),
                         col_names = c("chr", "start", "end", "ratio"), skip = 1)
      
      segs_B <- segs_B %>% filter(!chr %in% c("X", "Y", "23", "24")) 
   } else if (tumor_input == "sWGS"){
      segs_B <- read_tsv(paste0(datadir2, str_subset(dir(datadir2, pattern = sample2), "segments_mask.bed")), col_types = c("fdddd"),
                         col_names = c("chr", "start", "end", "ratio", "zscore"))
      segs_B <- segs_B %>% filter(!chr %in% c("X", "Y", "23", "24")) 
   }
   # 
   # segs_B <- read_tsv(paste0(datadir2,"/",sample2,"_", binsize, "_segments.tsv"), col_types = c("fddd"),
   #                      col_names = c("chr", "start", "end", "ratio"), skip = 1)
   # 
   # segs_B <- segs_B %>% filter(!chr %in% c("X", "Y", "23", "24")) 
   
   tmp <- inner_join(tmp_A, tmp_B)
   tmp$abslog2diffPerBin <- abs(tmp$ratio_tumor - tmp$ratio_cfDNA)
   segdiff <- nrow(segs_B) - nrow(segs_A)
   
   meanDiffPerBin <- mean(tmp$abslog2diffPerBin*segdiff)
   cumulDiffPerBin <- sum(tmp$abslog2diffPerBin*segdiff)
   data.frame(cfDNA_ID = sample1, tumorDNA_ID = sample2, mean_abs_diff_log2 = meanDiffPerBin, cumulDiffPerBin = cumulDiffPerBin)
}

makeBinsComparison <- function(datadir1, datadir2, sample1, sample2, patientID){
   
   bins_top <- read_tsv(paste0(datadir1, str_subset(dir(datadir1, pattern = sample1), "bins_mask")), col_types = c("fdd?d"),
                        col_names = c("chr", "start", "end", "id", "ratio"), skip = 1)
   bins_top <- bins_top %>% filter(!chr %in% c("X", "Y", "23", "24"))%>% mutate(SampleID = sample1)
   
   if (tumor_input == "array_BE" | tumor_input == "array_CZ" | tumor_input == "WES_FR" ){
      bins_bottom <-  read_tsv(paste0(datadir2,"/",sample2,"_", binsize, ".tsv"), col_types = c("fddd"),
                               col_names = c("chr", "start", "end", "ratio"))
      bins_bottom <- bins_bottom %>% filter(!chr %in% c("X", "Y", "23", "24")) %>% mutate(SampleID = sample2)
      bins_bottom$chr <- factor(bins_bottom$chr, levels = chr_order)
      
   } else if (tumor_input == "sWGS"){
      bins_bottom <-  read_tsv(paste0(datadir2, str_subset(dir(datadir2, pattern = sample2), "bins_mask")), col_types = c("fdd?d"),
                               col_names = c("chr", "start", "end", "id", "ratio"))
      bins_bottom <- bins_bottom %>% filter(!chr %in% c("X", "Y", "23", "24"))%>% mutate(SampleID = sample2) %>% select(-id)
      bins_bottom$chr <- factor(bins_bottom$chr, levels = chr_order)
   }
   
   
   corplot_df_bins <- full_join(bins_top, bins_bottom, by = c("chr", "start", "end")) %>% select(-chr, -start, -end)
   colnames(corplot_df_bins) <- c("id", "L2R_cfDNA", "CFD_ID", "L2R_tumorDNA", "tumorDNA_ID")
   corplot_df_bins$PatientID <- patientID
   corplot_df_bins
   
}


if (!file.exists("./data/alldiffs.tsv")){
   alldiffs <- data.frame()
   L2Rcomparison <- data.frame()
   for (row in 1:nrow(sample_annotation)){
      sample1 <- sample_annotation[row,]$CFD_ID
      sample2 <- sample_annotation[row,]$tumorDNA_ID
      patientID <-  sample_annotation[row,]$PatientID
      tissueplatform <- sample_annotation[row,]$tumorDNA_assay_detail
      tumortype <- sample_annotation[row,]$TumorType
      datadir1 <- datadir_sWGS_cfDNA
      if (sample_annotation[row,]$tumorDNA_assay == "array_BE"){
         tumor_input = "array_BE"
         datadir2 <-  datadir_arrayBE
      } else if (sample_annotation[row,]$tumorDNA_assay == "array_CZ"){
         tumor_input = "array_CZ"
         datadir2 <-  datadir_arrayCZ
      } else if (sample_annotation[row,]$tumorDNA_assay == "WES_FR"){
         tumor_input = "WES_FR"
         datadir2 <-  datadir_WES
      } else if (sample_annotation[row,]$tumorDNA_assay == "sWGS"){
         tumor_input = "sWGS"
         datadir2 <-  datadir_sWGS_tissue
      }
      if (length(dir(datadir2, pattern = sample2) > 0  | length(dir(datadir1, pattern = sample1) > 0))) {
         tmp <- makeLog2comparison(datadir1, datadir2, sample1, sample2, patientID)
         tmp_L2R <- makeBinsComparison(datadir1, datadir2, sample1, sample2, patientID)
         alldiffs <- rbind(alldiffs, tmp)
         L2Rcomparison <- rbind(L2Rcomparison, tmp_L2R)
      }
      print(paste0("Processed: ", row, "/", nrow(sample_annotation)))
   }
   write_tsv(alldiffs, "./data/alldiffs.tsv")
   write_tsv(L2Rcomparison, "./data/L2R.tsv")
} else {
   alldiffs  <- read_tsv("./data/alldiffs.tsv")
   L2Rcomparison <- read_tsv("./data/L2R.tsv")
}

cfDNAvsTissue <- left_join(cfDNAvsTissue, alldiffs, by = c("CFD_ID" = "cfDNA_ID", "tumorDNA_ID" = "tumorDNA_ID"))
L2Rcomparison <- left_join(cfDNAvsTissue, L2Rcomparison, by = c("CFD_ID" = "CFD_ID", "tumorDNA_ID" = "tumorDNA_ID", "PatientID" = "PatientID"))

```


```{r}
if (!file.exists("./data/merged_segments.tsv")){
   read_segments <- function(datadir){
      files<-list.files(c(datadir),recursive=TRUE)
      files<-files[grep("egments_per_[0-9]+kb_mask", files)]
      tmp_heatmap <- data.frame()
      for(i in 1:length(files)){
         name_sample <- gsub("_segments_per_[0-9]+kb_mask.tsv", "", files[i])
         print(name_sample)
         tmp <-  read_tsv(paste0(datadir,files[i]), col_types = c("fddd"),
                          col_names = c("chr", "start", "end", "ratio"), skip = 1)
         
         colnames(tmp)<- c("chr", "start", "end", "ratio")
         tmp <- tmp %>% filter(!chr %in% c("X", "Y", "23", "24"))
         tmp$SampleID <- name_sample
         tmp$meanLog2 <- get.cpa.modified2(tmp)
         if (nrow(tmp_heatmap) == 0){
            tmp_heatmap <- tmp
         } else {
            tmp_heatmap <- rbind(tmp_heatmap, tmp)
         }
      }
      return(tmp_heatmap)
   }
   df_heatmap_init <- data.frame()
   df_heatmap_init <- rbind(df_heatmap_init, read_segments(datadir_sWGS_cfDNA))
   df_heatmap_init <- rbind(df_heatmap_init, read_segments(datadir_WES))
   df_heatmap_init <- rbind(df_heatmap_init, read_segments(datadir_sWGS_tissue))
   df_heatmap_init <- rbind(df_heatmap_init, read_segments(datadir_arrayCZ))
   df_heatmap_init <- rbind(df_heatmap_init, read_segments(datadir_arrayBE))
   
   df_heatmap_healthy <- data.frame()
   df_heatmap_healthy <- rbind(df_heatmap_healthy, read_segments(datadir_healthy))
   write_tsv(df_heatmap_init, "./data/merged_segments.tsv")
   write_tsv(df_heatmap_healthy, "./data/merged_segments_healthy.tsv")
} else {
   df_heatmap_init <- read_tsv("./data/merged_segments.tsv")
   df_heatmap_healthy <- read_tsv("./data/merged_segments_healthy.tsv")
}

df_heatmap_init$SampleID <- gsub("_.*", "", df_heatmap_init$SampleID)
df_heatmap_init <- df_heatmap_init 

a <- df_heatmap_init %>% distinct(meanLog2, SampleID)
cfDNAvsTissue <- left_join(cfDNAvsTissue, a, by = c("CFD_ID" = "SampleID"))
cfDNAvsTissue <- left_join(cfDNAvsTissue, a, by = c("tumorDNA_ID" = "SampleID"))
cfDNAvsTissue$deltaLog2 <- abs(cfDNAvsTissue$meanLog2.x - cfDNAvsTissue$meanLog2.y)
a <- NULL

```


# Difference plot between cfDNA and tumor DNA
```{r}
tumor = "nephroblastoma"
makeBarTumorvscfDNA <- function(tumor){
   barPlot <- df_heatmap_init
   sampleFilt_a <- sample_annotation_long %>% filter(cfDNA_HMW_ratio > 5) %>% filter(TumorType == tumor & biomaterial == "cfDNA") %>% pull(SampleID)
   barPlot_a <- barPlot %>% filter(SampleID %in% sampleFilt_a)
   barPlot_a <- barPlot_a %>% group_by(chr, start) %>%
      summarise(mean = mean(ratio, na.rm = TRUE),
                sd = sd(ratio, na.rm = TRUE),
                n = n()) %>%
      mutate(se = sd / sqrt(n),
             lower.ci = mean - qt(1 - (0.05 / 2), n - 1) * se,
             upper.ci = mean + qt(1 - (0.05 / 2), n - 1) * se) 
   barPlot_a$biomaterial <- "cfDNA"
   
   sampleFilt_b <- sample_annotation_long %>% filter(cfDNA_HMW_ratio > 1) %>% filter(TumorType == tumor & biomaterial == "tumor DNA") %>% pull(SampleID)
   barPlot_b <- barPlot %>% filter(SampleID %in% sampleFilt_b)
   barPlot_b <- barPlot_b %>% group_by(chr, start) %>%
      summarise(mean = mean(ratio, na.rm = TRUE),
                sd = sd(ratio, na.rm = TRUE),
                n = n()) %>%
      mutate(se = sd / sqrt(n),
             lower.ci = mean - qt(1 - (0.05 / 2), n - 1) * se,
             upper.ci = mean + qt(1 - (0.05 / 2), n - 1) * se) 
   barPlot_b$biomaterial <- "tumor DNA"
   
   
   ggplot() + 
      theme_bw() + 
      # labs(title = paste0(patientID, " - ", tissueplatform, " tissue - ", sample2, " - ",cpa.calc, ": ", cpa_bottom, " - ", tumortype), y = "log2(ratio)") +
      geom_bar(data = barPlot_a, aes(x = start, y = mean, col = biomaterial, fill = biomaterial), stat = "identity", position = "dodge", alpha = 1, size = 0) +
      geom_bar(data = barPlot_b, aes(x = start, y = mean, col = biomaterial, fill = biomaterial), stat = "identity", position = "dodge", alpha = 0.5, size = 0) +
      lims(y = c(-.6,.6))+ 
      labs(title = paste0(tumor, " (n = ", length(sampleFilt_b),")"), y = "mean of log2(ratio) across all samples")+
      theme(panel.spacing.x = unit(0, "lines"),
            panel.spacing.y = unit(0, "lines"),
            axis.title.x =  element_blank(),
            axis.text.x =  element_blank(),
            axis.ticks.x =  element_blank(),
            strip.background = element_rect(color = "white", fill = "white"),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.background = element_blank()) + 
      geom_hline(yintercept = 0, linetype = "dashed", col = "gray") +
      facet_wrap(~chr, strip.position = "bottom", scales ="free_x", nrow = 1)}

p1 <- makeBarTumorvscfDNA("neuroblastoma")
p2 <- makeBarTumorvscfDNA("nephroblastoma")
p3 <- makeBarTumorvscfDNA("osteosarcoma")
p4 <- makeBarTumorvscfDNA("rhabdomyosarcoma")
p5 <- makeBarTumorvscfDNA("Ewing sarcoma")
```


```{r, fig.width= 8, fig.height= 16}
ggarrange(p1, p2, p4, p5, ncol = 1, common.legend = TRUE)

ggsave("./plots/comparativeplot.png", plot = ggplot2::last_plot(), dpi = 300, width = 8, height = 12)
ggsave("./plots/comparativeplot.svg", plot = ggplot2::last_plot(), dpi = 300, width = 8, height = 12)
ggsave("./plots/comparativeplot.pdf", plot = ggplot2::last_plot(), dpi = 300, width = 8, height = 12)
```

# Sequencing quality control metrics
## Mapped reads
```{r}
mapped_reads <- read_tsv("./data/logs/multiqc_data/multiqc_samtools_stats.txt")
mapped_reads$Sample <- gsub("_.*", "", mapped_reads$Sample)
mapped_reads <- inner_join(mapped_reads, sample_annotation_long, by = c("Sample" = "SampleID"))


ggplot(mapped_reads, aes(y = biomaterial, x =  reads_mapped_percent, fill = biomaterial )) + 
   geom_density_ridges2(scale = 0.5, alpha = 0.5, 
                        jittered_points = TRUE, position = "raincloud",
                        aes(point_color = biomaterial, point_fill = biomaterial)) +
   geom_boxplot(alpha = 0.5, width = 0.1) + theme_bw() + 
   lims(x = c(0,100)) +
   labs(title = "Alignment percentage across all samples", x = "reads mapped with bwa mem (%)")

mapped_reads %>% filter(reads_mapped_percent < 24) %>% select(Sample, reads_mapped_percent)

ggsave("./plots/mapped_reads.png", plot = ggplot2::last_plot(), dpi = 300, width = 6, height = 4)
ggsave("./plots/mapped_reads.svg", plot = ggplot2::last_plot(), dpi = 300, width = 6, height = 4)
ggsave("./plots/mapped_reads.pdf", plot = ggplot2::last_plot(), dpi = 300, width = 6, height = 4)
```


## Duplicate percentage
```{r}
duplicated_reads <- read_tsv("./data/logs/multiqc_data/multiqc_picard_dups.txt")
duplicated_reads$Sample <- gsub("_.*", "", duplicated_reads$Sample)
duplicated_reads <- inner_join(duplicated_reads, sample_annotation_long, by = c("Sample" = "SampleID"))
duplicated_reads$duplicate_percentage <- duplicated_reads$UNPAIRED_READ_DUPLICATES/duplicated_reads$UNPAIRED_READS_EXAMINED*100

ggplot(duplicated_reads, aes(y = biomaterial, x =  UNPAIRED_READ_DUPLICATES/UNPAIRED_READS_EXAMINED*100, fill = biomaterial )) + 
   geom_density_ridges2(scale = 0.5, alpha = 0.5, 
                        jittered_points = TRUE, position = "raincloud",
                        aes(point_color = biomaterial, point_fill = biomaterial)) +
   geom_boxplot(alpha = 0.5, width = 0.1) + theme_bw() + 
   lims(x = c(0,100)) +
   labs(title = "duplicate percentage across all samples", x = "duplicate reads with Picard (%)") 

ggsave("./plots/duplicate_perc.png", plot = ggplot2::last_plot(), dpi = 300, width = 6, height = 4)
ggsave("./plots/duplicate_perc.svg", plot = ggplot2::last_plot(), dpi = 300, width = 6, height = 4)
ggsave("./plots/duplicate_perc.pdf", plot = ggplot2::last_plot(), dpi = 300, width = 6, height = 4)

ggplot(mapped_reads, aes(y = biomaterial, x =  reads_mapped, fill = biomaterial )) + 
   geom_density_ridges2(scale = 0.5, alpha = 0.5, 
                        jittered_points = TRUE, position = "raincloud",
                        aes(point_color = biomaterial, point_fill = biomaterial)) +
   geom_boxplot(alpha = 0.5, width = 0.1) + theme_bw() + 
   #lims(x = c(0,100)) +
   labs(title = "absolute number of mapped reads after deduplication", x = "number of remaining reads") 
```

# CNAs in tumor tissue

```{r}
ggplot(cfDNAvsTissue, aes(x = TumorAbbrev, y = cpa_tumorDNA, col = tumorDNA_CNAs_consensus, label = PatientID))+ theme_bw()  +
   theme(axis.text.x = element_text(angle = 45,hjust=1))  + geom_boxplot(alpha = 0.4, aes(col = NULL)) + geom_beeswarm(alpha = 0.7)  + facet_wrap(~TumorGroup, scales = "free_x") + labs(y = "CPAm tumorDNA")

ggsave("./plots/CPA_tumor.png", plot = ggplot2::last_plot(), dpi = 300, width = 10, height = 6)
ggsave("./plots/CPA_tumor.svg", plot = ggplot2::last_plot(), dpi = 300, width = 10, height = 6)
ggsave("./plots/CPA_tumor.pdf", plot = ggplot2::last_plot(), dpi = 300, width = 10, height = 6)

ggplot(cfDNAvsTissue %>% filter(tumorDNA_CNAs_rater1 != "NA"), aes(x = cpa_tumorDNA, fill = tumorDNA_CNAs_consensus)) + geom_boxplot(alpha = 0.5) + facet_wrap(~ tumorDNA_assay + tumorDNA_biomaterial) + theme_bw()

```

- The number of tumor samples with CNAs:  `r cfDNAvsTissue %>% filter(tumorDNA_CNAs_consensus == "TRUE") %>% pull(tumorDNA_CNAs_consensus) %>% length() `
- The number of tumor samples with no CNAs:  `r cfDNAvsTissue %>% filter(tumorDNA_CNAs_consensus == "FALSE") %>% pull(tumorDNA_CNAs_consensus) %>% length() `
- The number of cfDNA samples with CNAs:  `r cfDNAvsTissue %>% filter(CNA_cfDNA == "CNA") %>% pull(CNA_cfDNA) %>% length() `
- The number of cfDNA samples being flat:  `r cfDNAvsTissue %>% filter(CNA_cfDNA == "flat") %>% pull(CNA_cfDNA) %>% length() `
- The median CPAm in tumors: `r median(cfDNAvsTissue$cpa_tumorDNA)` and Q1/Q3 `r quantile(cfDNAvsTissue$cpa_tumorDNA)`
- The median CPAm in cfDNA: `r median(cfDNAvsTissue$cpa_cfDNA)` and Q1/Q3 `r quantile(cfDNAvsTissue$cpa_cfDNA)`

# Comparison of cfDNA vs tissue

## Crosstable of cfDNA and tumor CNAs {.tabset}
### All samples
```{r}
cfDNAvsTissue$CNA_cfDNA_bool <- ifelse(cfDNAvsTissue$CNA_cfDNA == "CNA", TRUE, FALSE)
cfDNAvsTissue$tumorDNA_CNAs_consensus <- as.logical(cfDNAvsTissue$tumorDNA_CNAs_consensus)
sjt.xtab(cfDNAvsTissue$tumorDNA_CNAs_consensus , cfDNAvsTissue$CNA_cfDNA_bool, var.labels=c("tumor", "cfDNA"), value.labels = list(c("neutral", "CNA"), c("neutral", "CNA")), title = "crosstable for all samples")
```

### cfDNA/HMW ratio < 1
```{r}
sjt.xtab(cfDNAvsTissue %>% filter(cfDNA_HMW_ratio < 1) %>% pull(tumorDNA_CNAs_consensus) , cfDNAvsTissue %>% filter(cfDNA_HMW_ratio < 1) %>% pull(CNA_cfDNA_bool), var.labels=c("tumor", "cfDNA"), value.labels = list(c("neutral", "CNA"), c("neutral", "CNA")), title = "crosstable for samples with a cfDNA/HMW ratio < 1")
```

### cfDNA/HMW ratio 1-5
```{r}
sjt.xtab(cfDNAvsTissue %>% filter(cfDNA_HMW_ratio >= 1 & cfDNA_HMW_ratio < 5) %>% pull(tumorDNA_CNAs_consensus) , cfDNAvsTissue %>% filter(cfDNA_HMW_ratio >= 1 & cfDNA_HMW_ratio < 5) %>% pull(CNA_cfDNA_bool), var.labels=c("tumor", "cfDNA"), value.labels = list(c("neutral", "CNA"), c("neutral", "CNA")), title = "crosstable for samples with a cfDNA/HMW ratio [1,5]")
```

### cfDNA/HMW ratio > 5
```{r}
sjt.xtab(cfDNAvsTissue %>% filter(cfDNA_HMW_ratio > 5) %>% pull(tumorDNA_CNAs_consensus) , cfDNAvsTissue %>% filter(cfDNA_HMW_ratio >= 5 ) %>% pull(CNA_cfDNA_bool), var.labels=c("tumor", "cfDNA"), value.labels = list(c("neutral", "CNA"), c("neutral", "CNA")), title = "crosstable for samples with a cfDNA/HMW ratio > 5 ")
```


```{r}
cfDNAvsTissue$CNA_cfDNA_bool <- ifelse(cfDNAvsTissue$CNA_cfDNA == "CNA", TRUE, FALSE)
cfDNAvsTissue$tumorDNA_CNAs_consensus <- as.logical(cfDNAvsTissue$tumorDNA_CNAs_consensus)
sjt.xtab(cfDNAvsTissue$tumorDNA_CNAs_consensus , cfDNAvsTissue$CNA_cfDNA_bool, var.labels=c("tumor", "cfDNA"), value.labels = list(c("neutral", "CNA"), c("neutral", "CNA")))
```

## Correlation of CPAm and CPA

Where possible (shallow WGS samples, n = 82), the correlation between the CPA and CPAm was calculated and yielded a Pearson R of 0.74 and a spearman rho of 0.86. The CPAm threshold of copy number neutral (“normal” or “flat”) at the 1% false discovery level in cfDNA was calculated on the cohort of healthy controls (individuals above 18 years old with no cancer diagnosis in their past medical history) from Raman et al. and was found to be 0.354. With this threshold, there are 4/82 cfDNA samples that were labeled discordant, i.e., copy number neutral with CPA and copy number aberrations with CPAm. Upon manual inspection, 3 out of these 4 samples contain visible CNAs, while 1 out of 4 samples is copy number neutral.

```{r}
CPA_df <- read_csv("./data/sWGS_tissue/CPA_all.csv")

spearman_cor <- cor(CPA_df$CPA, CPA_df$CPAm, method = "spearman")

ggplot(CPA_df, aes(x = CPA, y = CPAm)) + geom_point() + geom_smooth(method = 'lm') + theme_bw() + labs(title = paste0("Correlation between modified CPA (CPAm) and original CPA"), subtitle = paste0("Spearman rho = ", round(spearman_cor, 4))) + annotate("rect",ymin = 0, ymax = FDR_CPAm, xmin = -Inf, xmax = Inf, alpha=0.3, fill="grey") + scale_shape_manual(values=c(1,1,1,1,1)) + annotate("rect",xmin = 0, xmax = FDR_CPA, ymin = -Inf, ymax = Inf, alpha=0.3, fill="grey") + scale_shape_manual(values=c(1,1,1,1,1))
```

The samples that were discordant between CPA (1% FDR = `r FDR_CPA`) and CPAm (1% FDR = `r FDR_CPAm`) are:

```{r}
CPA_df %>% filter(CPA > FDR_CPA & CPAm < FDR_CPAm | CPA < FDR_CPA & CPAm > FDR_CPAm )
```

## Distribution of CPA in tissue DNA and cfDNA
```{r}
conflict_prefer("melt", "data.table")
CPAm_ridge <- ggplot(
   cfDNAvsTissue %>% select(cpa_tumorDNA, cpa_cfDNA, cpa_type)
   %>% filter(cpa_type == "CPAm") %>% melt(), 
   aes(y = variable, x = value, fill = variable)) +
   geom_density_ridges2(scale = 0.8, panel_scaling = FALSE,
                        point_alpha = 0.5, alpha = .2, 
                        aes(point_color = variable, point_fill = variable),
                        jittered_points = TRUE, position = "raincloud") +
   theme_bw() + 
   labs(fill = "tumor", point_fill = "tumor", point_color = "tumor", x = "CPAm", y = "tumor", title = "modified CPA")  + 
   geom_boxplot(alpha = 0.4, width = 0.1)

CPAm_ridge

ggsave("./plots/CPAm_ridgeplot.png", plot = ggplot2::last_plot(), dpi = 300, width = 8, height = 6)
ggsave("./plots/CPAm_ridgeplot.svg", plot = ggplot2::last_plot(), dpi = 300, width = 8, height = 6)
ggsave("./plots/CPAm_ridgeplot.pdf", plot = ggplot2::last_plot(), dpi = 300, width = 8, height = 6)
```

## cfDNA/HMW ratio distribution
```{r}
ggplot(cfDNAvsTissue, aes(x = as.numeric(cfDNA_HMW_ratio))) + 
   geom_density(alpha = 0.5, fill = "gray", col = "black") + theme_bw() + scale_x_log10()+labs(x = "cfDNA/HMW ratio") 


ggplot(cfDNAvsTissue, aes(col = TumorGroup, y = as.numeric(cpa_cfDNA), x = as.numeric(cfDNA_HMW_ratio))) + 
   geom_point(alpha = 0.8) + theme_bw() + 
   facet_wrap(~cpa_type, scales = "free_y") + labs(col = "cfDNA/HMW ratio")  + 
   scale_x_log10() + 
   labs(col = "tumor type", y = "CPAm cfDNA", x = "cfDNA/HMW ratio") 

ggsave("./plots/CPAm_cfDNAvsSampleQ.png", plot = ggplot2::last_plot(), dpi = 300, width = 8, height = 6)
ggsave("./plots/CPAm_cfDNAvsSampleQ.svg", plot = ggplot2::last_plot(), dpi = 300, width = 8, height = 6)
ggsave("./plots/CPAm_cfDNAvsSampleQ.pdf", plot = ggplot2::last_plot(), dpi = 300, width = 8, height = 6)
```

## CPA cfDNA vs Pearson R ~ cfDNA/HMW ratio
```{r}
BeforeAfter <- cfDNAvsTissue

BeforeAfter$cfDNA_HMW_ratio <- as.character(BeforeAfter$cfDNA_HMW_ratio )
BeforeAfter$colLabel <- ifelse(cfDNAvsTissue$cpa_tumorDNA > FDR_CPAm & cfDNAvsTissue$cpa_cfDNA < FDR_CPAm, "label",  "")
BeforeAfter_melt <- BeforeAfter %>% select(TumorType, colLabel, PatientID, cpa_tumorDNA, cpa_cfDNA, cpa_type, TumorGroup, tumorDNA_assay_detail, cfDNA_HMW_ratio) %>% filter(cpa_type == "CPAm") %>% melt()
BeforeAfter_melt$cfDNA_HMW_ratio <- ifelse(BeforeAfter_melt$variable == "cpa_tumorDNA", 0, as.numeric(BeforeAfter_melt$cfDNA_HMW_ratio))

ggplot(BeforeAfter_melt, 
       aes(col = colLabel, x = variable, y = value, group = PatientID, alpha = colLabel)) + 
   geom_line(position = position_dodge(width = 0.5)) + 
   geom_point(data = BeforeAfter_melt, position = position_dodge(width = 0.5), aes(size = cfDNA_HMW_ratio)) + 
   scale_y_log10() + theme_bw() + 
   facet_wrap(~TumorGroup, ncol = 2) + 
   labs(size = "cfDNA/HMW ratio", x = "biomaterial", y = "CPAm") +
   scale_color_manual(values=c("gray", "navyblue")) + 
   scale_alpha_discrete(range = c(0.5, 0.9)) + theme(legend.position = "right")

ggsave("./plots/beforeAfter.png", plot = ggplot2::last_plot(), dpi = 300, width = 8, height = 9)
ggsave("./plots/beforeAfter.svg", plot = ggplot2::last_plot(), dpi = 300, width = 8, height = 9)
ggsave("./plots/beforeAfter.pdf", plot = ggplot2::last_plot(), dpi = 300, width = 8, height = 9)

cfDNAvsTissue$labelfig <- ifelse(cfDNAvsTissue$PatientID == "patient_079", "patient_079",
                                 ifelse(cfDNAvsTissue$PatientID == "patient_212", "patient_212",
                                        ifelse(cfDNAvsTissue$PatientID == "patient_077", "patient_077",
                                               ifelse(cfDNAvsTissue$PatientID == "patient_196", "patient_196", ""))))
p_CPAvsR <- ggplot(cfDNAvsTissue, aes(x =  as.numeric(cpa_cfDNA), y = pearsonR, size = as.numeric(cfDNA_HMW_ratio), shape = tumorDNA_assay_detail, col = tumorDNA_assay_detail, label = labelfig)) + 
   geom_point(alpha = 1) + theme_bw() + 
   labs(size = "cfDNA/HMW ratio", x = "CPAm cfDNA", y = "Pearson R", col = "tissue DNA platform", shape = "tissue DNA platform") + theme(legend.position="left")+
   geom_vline(data=cfDNAvsTissue %>% filter(cpa_type == "CPAm"), aes(xintercept = FDR_CPAm), linetype = "dashed")+
   annotate("rect",xmin = 0, xmax = FDR_CPAm, ymin = -Inf, ymax = Inf, alpha=0.3, fill="grey") + scale_shape_manual(values=c(1,1,1,1,1)) +
   scale_color_manual(values = c("#F8766D", "#7CAE00", "#7CAE00", "#00BFC4", "#C77CFF")) + geom_text_repel(size = 4, show.legend = FALSE)

#p_CPAvsR

#ggarrange(CPAm_ridge, p_CPAvsR, legend = "bottom")
```

## Tumor types in dataset
```{r}
cfDNAvsTissue$TumorType <- gsub("malignant rhabdoid tumor of the kidney", "MRTK", cfDNAvsTissue$TumorType)
p1 <- ggplot(cfDNAvsTissue %>% distinct(PatientID, .keep_all = TRUE), aes(fill = SampleOrigin, x = reorder(TumorType, TumorType, function(x) -length(x)))) + theme_bw() +
   geom_bar(alpha = 0.9) + labs(x = "", title = "all tumor types", y = "number of unique cases", fill = NULL)+ 
   theme(axis.text.x = element_text(angle = 45,hjust=1))  +
   scale_fill_manual(values = qg_palette("USopen")[c(4,1:3)])

p2 <- ggplot(cfDNAvsTissue, aes(fill = SampleOrigin, x = reorder(tumorDNA_assay_detail, tumorDNA_assay_detail, function(x) -length(x)))) + theme_bw() +
   geom_bar(alpha = 0.9) + labs(x = "", title = "tissue DNA platform", y = "number of samples", fill = NULL)+ 
   theme(axis.text.x = element_text(angle = 45,hjust=1))  +
   scale_fill_manual(values = qg_palette("USopen")[c(4,1:3)])

ggarrange(p1, p2, labels = c("A", "B"), align = c("h"), widths = c(0.6, 0.4), common.legend = TRUE, legend = "top")

ggsave("./plots/tumorsInDataset.png", plot = ggplot2::last_plot(), dpi = 300, width = 10, height = 6)
ggsave("./plots/tumorsInDataset.pdf", plot = ggplot2::last_plot(), dpi = 300, width = 10, height = 6)

cfDNAvsTissue_select <- cfDNAvsTissue_withHealthy %>% filter(TumorType %in% c("neuroblastoma", "nephroblastoma", "osteosarcoma", "rhabdomyosarcoma", "Ewing sarcoma", "healthy") & (cpa_type == "CPAm")) %>% filter(as.numeric(cfDNA_HMW_ratio) >= 0)

ridgeplot_cfDNA_per_tumor <- ggplot(
   cfDNAvsTissue_select %>% filter(TumorType != "healthy"), 
   aes(y = TumorType, x = cpa_cfDNA, fill = TumorType)) +
   geom_density_ridges(show.legend = FALSE, scale = 0.8, panel_scaling = FALSE,
                       point_alpha = 0.7, alpha = .2, point_shape = 1,
                       aes(point_color = TumorType,
                           point_fill = TumorType,
                           point_size = as.numeric(cfDNA_HMW_ratio)), 
                       jittered_points = TRUE, position = "raincloud") +
   theme_bw() + 
   labs(point_size = "cfDNA/HMW ratio", fill = "tumor", point_fill = "tumor", point_color = "tumor", x = "CPAm cfDNA", y = "tumor") + 
   geom_boxplot(alpha = 0.4, width = 0.2, show.legend = FALSE, outlier.shape = NA) + 
   theme(legend.position="right")+
   geom_vline(data=cfDNAvsTissue %>% filter(cpa_type == "CPAm"), alpha = 0.6, aes(xintercept = FDR_CPAm), linetype = "dashed")+
   annotate("rect",xmin = 0, xmax = FDR_CPAm, ymin = -Inf, ymax = Inf, alpha=0.3, fill="grey")  + lims(x = c(0,NA))


ridgeplot_tissueDNA_per_tumor <- ggplot(
   cfDNAvsTissue_select %>% filter(TumorType != "healthy"), 
   aes(y = TumorType, x = cpa_tumorDNA, fill = TumorType)) +
   geom_density_ridges(show.legend = FALSE, scale = 0.8, panel_scaling = FALSE,
                       point_alpha = 0.7, alpha = .2, point_shape = 1,
                       aes(point_color = TumorType, point_fill = TumorType), jittered_points = TRUE, position = "raincloud") +
   theme_bw() + 
   labs(y="", point_size = "cfDNA/HMW ratio", fill = "tumor", point_fill = "tumor", point_color = "tumor", x = "CPAm tumorDNA", y = "tumor") + 
   geom_boxplot(alpha = 0.4, width = 0.2, show.legend = FALSE, outlier.shape = NA) + 
   theme(legend.position="right") + theme(axis.text.y = element_blank())
```

## Impact on neuroblastoma risk stratification {.tabset}
### All samples

```{r}
MYCN <- sample_annotation %>% dplyr::filter(MYCN_amplification_cfDNA != "NA" & MYCN_amplification_tumor != "NA")
sjt.xtab(MYCN$MYCN_amplification_cfDNA, MYCN$MYCN_amplification_tumor, var.labels=c("cfDNA", "tumor"), value.labels = list(c("MYCN neutral", "MYCN gain"), c("MYCN neutral", "MYCN gain")), show.cell.prc = TRUE, show.row.prc = TRUE, show.col.prc = TRUE)
```

### cfDNA/HMW ratio above 1
```{r}
MYCN_highqual <- MYCN %>% dplyr::filter(cfDNA_HMW_ratio > 1)
sjt.xtab(MYCN_highqual$MYCN_amplification_cfDNA, MYCN_highqual$MYCN_amplification_tumor, var.labels=c("cfDNA", "tumor"), value.labels = list(c("MYCN neutral", "MYCN gain"), c("MYCN neutral", "MYCN gain")), show.cell.prc = TRUE, show.row.prc = TRUE, show.col.prc = TRUE)
```


## Impact on 1q gain in nephroblastoma
### All samples

```{r}
nephr_1q <- sample_annotation %>% dplyr::filter(`gain_1q_WT_cfDNA` != "NA" & `gain_1q_WT_tumor`  != "NA")
sjt.xtab(nephr_1q$`gain_1q_WT_cfDNA`, nephr_1q$`gain_1q_WT_tumor` , var.labels=c("cfDNA", "tumor"), value.labels = list(c("1q neutral", "1q gain"), c("1q neutral", "1q gain")), show.cell.prc = TRUE, show.row.prc = TRUE, show.col.prc = TRUE)
```

### cfDNA/HMW ratio above 1
```{r}
nephr_1q_highqual <- nephr_1q %>% dplyr::filter(cfDNA_HMW_ratio > 1)
sjt.xtab(nephr_1q_highqual$`gain_1q_WT_cfDNA`, nephr_1q_highqual$`gain_1q_WT_tumor` , var.labels=c("cfDNA", "tumor"), value.labels = list(c("1q neutral", "1q gain"), c("1q neutral", "1q gain")), show.cell.prc = TRUE, show.row.prc = TRUE, show.col.prc = TRUE)
```

# Generalized Additive Modelling
## Data cleaning


```{r}
library(sjPlot)
library(gamm4)
library(lme4)
library(lmerTest)
library(mgcv)
library(tidymv)
library(mgcViz)


cfDNA_GAM <- L2Rcomparison %>% select(UniqueID, tumorDNA_assay_detail, TumorType, cfDNA_HMW_ratio, PatientID, metastatic, mean_abs_diff_log2,  CFD_ID,  L2R_cfDNA, L2R_tumorDNA, TumorGroup, id, plasma_prep_protocol, tumorDNA_assay_detail)

cfDNA_GAM <- cfDNA_GAM  %>% filter(TumorType %in% c(sample_annotation  %>% group_by(TumorType) %>% count() %>% filter(n >= 5) %>% pull(TumorType))) %>% filter(TumorGroup != "brain tumor")

cfDNA_GAM$TumorType <- gsub(" ", "", cfDNA_GAM$TumorType)
cfDNA_GAM$tumorDNA_assay_detail <- gsub(" ", "", cfDNA_GAM$tumorDNA_assay_detail)
cfDNA_GAM$metastatic <- gsub(" ", "", cfDNA_GAM$metastatic)
cfDNA_GAM$id <- as.factor(cfDNA_GAM$id)

```

- Number of total observations `r cfDNA_GAM %>% nrow()`

```{r}
cfDNA_GAM <- cfDNA_GAM  %>% filter(metastatic != "NA")
```

- NUmber of observations after removing all NAs: `r cfDNA_GAM %>% filter(!is.na(mean_abs_diff_log2) & !is.na(cfDNA_HMW_ratio) & !is.na(tumorDNA_assay_detail) & !is.na(metastatic)) %>% nrow()`

```{r}
cfDNA_GAM$TumorType <- as.factor(cfDNA_GAM$TumorType)
cfDNA_GAM$PatientID <- as.factor(cfDNA_GAM$PatientID)
cfDNA_GAM$UniqueID <- as.factor(cfDNA_GAM$UniqueID)
cfDNA_GAM$metastatic <- as.factor(cfDNA_GAM$metastatic)
cfDNA_GAM$cfDNA_HMW_ratio_log10 <- log10(cfDNA_GAM$cfDNA_HMW_ratio)# + 0.001)
cfDNA_GAM$tumorDNA_assay_detail <- as.factor(cfDNA_GAM$tumorDNA_assay_detail)
cfDNA_GAM$plasma_prep_protocol <- as.factor(cfDNA_GAM$plasma_prep_protocol)

cfDNA_GAM$quality_score <- ifelse(cfDNA_GAM$cfDNA_HMW_ratio < 1, "low",
                                  ifelse(cfDNA_GAM$cfDNA_HMW_ratio < 5 & cfDNA_GAM$cfDNA_HMW_ratio > 1, "medium", "high"))
cfDNA_GAM$quality_score <- as.factor(cfDNA_GAM$quality_score)

cfDNA_GAM$L2R_tumorDNA <- scale(cfDNA_GAM$L2R_tumorDNA, scale = FALSE)
cfDNA_GAM$L2R_cfDNA <- scale(cfDNA_GAM$L2R_cfDNA, scale = FALSE)
```

## Model specification
```{r}
if (!file.exists("L2RcfDNA_model_simple.rda")){
   Model <- bam(L2R_cfDNA ~ L2R_tumorDNA*cfDNA_HMW_ratio_log10 + L2R_tumorDNA*TumorType + L2R_tumorDNA*metastatic + s(PatientID, bs = "re"), data = cfDNA_GAM, nthreads = 3, family = scat(), discrete = TRUE, method = "fREML", gc.level = 2, drop.intercept = FALSE)
   saveRDS(Model, "L2RcfDNA_model_simple.rda")
} else {
   Model <- readRDS("L2RcfDNA_model_simple.rda")
}
```

## Summary
```{r}
#summary(Model)

#plot_model(Model, show.values = TRUE, value.offset = .3)

plot_model(Model, type = "est") + theme_bw()
ggsave("est.png", plot <- ggplot2::last_plot(), width = 10, height = 10)

plot_model(Model, type = "pred")+ theme_bw()
ggsave("pred.png", plot <- ggplot2::last_plot(), width = 10, height = 10)

#qual_interaction <- plot_model(Model, type = "pred", terms = c("L2R_tumorDNA","cfDNA_HMW_ratio_log10 [0:1 by = 0.1]")) + geom_abline(slope = 1, intercept = 0, linetype = "dashed")+ theme_bw() + labs(col = "cfDNA/HMW ratio (log10)", title = "predicted values of log2(ratio) in cfDNA", y = "log2(ratio) in cfDNA", x = "log2(ratio) in tumor DNA")
library(ggeffects)
mydf <- ggpredict(Model,terms = c("L2R_tumorDNA","cfDNA_HMW_ratio_log10 [0:2 by = 0.08]"))

qual_interaction <-  ggplot(mydf, aes(x, predicted, group = group, col = as.numeric(group))) +
   geom_line(size = 4) + theme_bw() + labs(col = "cfDNA/HMW ratio (log10)", title = "predicted values of log2(ratio) in cfDNA", y = "log2(ratio) in cfDNA", x = "log2(ratio) in tumor DNA")+ geom_abline(slope = 1, intercept = 0, linetype = "dashed")

ggsave("pred_ratio.png", plot <- ggplot2::last_plot(), width = 10, height = 10)

plot_model(Model, type = "pred", terms = c("L2R_tumorDNA", "TumorType"))+ geom_abline(slope = 1, intercept = 0, linetype = "dashed")+ theme_bw()
ggsave("pred_type.png", plot <- ggplot2::last_plot(), width = 10, height = 10)

plot_model(Model, type = "pred", terms = c("TumorType", "L2R_tumorDNA [-0.1, 0 , 0.1]"))+ theme_bw()
ggsave("pred_type2.png", plot <- ggplot2::last_plot(), width = 10, height = 10)

plot_model(Model, type = "pred", terms = c("L2R_tumorDNA", "metastatic")) + geom_abline(slope = 1, intercept = 0, linetype = "dashed")+ theme_bw()
ggsave("pred_meta.png", plot <- ggplot2::last_plot(), width = 10, height = 10)

plot_model(Model, type = "pred", terms = c("metastatic", "L2R_tumorDNA [-0.1, 0 , 0.1]"))+ theme_bw()
ggsave("pred_meta2.png", plot <- ggplot2::last_plot(), width = 10, height = 10)

#plot_model(Model, type = "slope", show.loess = FALSE)+ theme_bw()
#ggsave("pred_slope.png", plot <- ggplot2::last_plot(), width = 10, height = 10)

#plot_model(Model, type = "resid", show.loess = FALSE)+ theme_bw()
#ggsave("pred_resid.png", plot <- ggplot2::last_plot(), width = 10, height = 10)
```

## Visualisation of assumptions
```{r}
v <- getViz(Model)
print(plot(v) + l_points() + l_fitLine(), pages = 1)

check(v, a.hist = list(bins  = 100), a.respoi = list(alpha = 0.1))
```

## Regression coefficient table

```{r}
tab_model(Model, show.reflvl = TRUE, digits = 4,  file = "table_GAM.html")
```


# Heatmap {.tabset}

```{r}

df_heatmap_init = df_heatmap_init %>% select(-c(meanLog2))

# params to test function with
#sample_select <- c("osteosarcoma", "Ewing sarcoma", "rhabdomyosarcoma")
#width_px = 1200
#height_px = 1800
#sample_select <- "neuroblastoma"

makeHeatmap <- function(df_heatmap_init, sample_select, width_px, height_px, arrangehm = "tumor"){
   #df_heatmap = df_heatmap_init %>% filter(str_detect(SampleID, neuroblastoma))
   df_heatmap <- df_heatmap_init
   df_heatmap$bin <- paste0(as.character(df_heatmap$chr), ":", as.character(df_heatmap$start), "-", as.character(df_heatmap$end))
   
   # find cause of duplicates
   df_heatmap <- df_heatmap %>% select(-c(chr, start, end)) %>% group_by(SampleID) %>% distinct(bin, .keep_all = TRUE) %>% ungroup() %>% spread(key = c(bin), value = ratio)
   df_heatmap <- df_heatmap %>% 
      select(where(~!any(is.na(.))))
   #df_heatmap <- df_heatmap %>% distinct(SampleID, .keep_all = TRUE)
   
   colnames_hm <- paste0(gsub(":.*", "", colnames(df_heatmap[,2:ncol(df_heatmap)])))
   colnames_hm <- factor(colnames_hm, levels = chr_order)
   rownames_hm <- df_heatmap$SampleID
   
   
   sampleTypes <- sample_annotation_long %>% filter(SampleID  %in% rownames_hm)
   
   sampleTypes <- sampleTypes %>% dplyr::arrange(PatientID)
   
   df_heatmap <- inner_join(sampleTypes, df_heatmap)
   if (arrangehm == "pearsonR"){
      df_heatmap <- df_heatmap %>% dplyr::arrange(desc(pearsonR),PatientID, TumorType)
   }else{
      df_heatmap <- df_heatmap %>% dplyr::arrange(PatientID, TumorType)
   }
   df_heatmap <- df_heatmap %>% distinct(SampleID, `1:100400001-100600000`, 
                                         `1:107200001-107400000`,
                                         `1:113000001-113200000`,
                                         `1:117800001-118000000`, .keep_all = TRUE)
   if (arrangehm == "pearsonR"){
      df_heatmap <- df_heatmap %>% filter(PatientID %in% sample_select)
   } else{
      df_heatmap <- df_heatmap %>% filter(TumorType %in% sample_select)
   }
   
   df_heatmap$pearsonR <- round(df_heatmap$pearsonR, 2)
   #df_heatmap$pearsonR <- ifelse(df_heatmap$biomaterial == "cfDNA", NA, df_heatmap$pearsonR)
   #    cbind(sample_annotation$sWGS, sample_annotation$TumorType, rep("sWGS", nrow(sample_annotation))),
   #    cbind(sample_annotation$array, sample_annotation$TumorType, rep("array", nrow(sample_annotation)))
   # ))
   # colnames(sampleTypes) <- c("SampleID", "tumor", "type")
   
   ht_opt(
      legend_title_gp = gpar(fontsize = 20, fontface = "bold"),
      legend_labels_gp = gpar(fontsize = 20),
      ROW_ANNO_PADDING = unit(8,"mm")
   )
   
   tumor_col <- colorRampPalette(colors = brewer.pal(12, "Paired")) (length(levels(as.factor(df_heatmap$TumorType))))
   names(tumor_col) <- levels(as.factor(df_heatmap$TumorType))
   
   platform_col <- colorRampPalette(colors = brewer.pal(9, "Set1")) (length(levels(as.factor(df_heatmap$assayDetail))))
   names(platform_col) <- levels(as.factor(df_heatmap$assayDetail))
   
   biomat_col <- colorRampPalette(colors = brewer.pal(3, "Set3")) (length(levels(as.factor(df_heatmap$biomaterial))))
   names(biomat_col) <- levels(as.factor(df_heatmap$biomaterial))
   
   source_col <- colorRampPalette(colors = brewer.pal(5, "Set2")) (length(levels(as.factor(df_heatmap$source))))
   names(source_col) <- levels(as.factor(df_heatmap$source))
   
   pearsonR_col <- colorRamp2(c(-0.1, 1), colors = c("white", "purple"))
   
   ha = HeatmapAnnotation(
      CPAm = anno_barplot(df_heatmap$CPAm, gp = gpar(fill = 2, col = 2)),
      pearsonR = df_heatmap$pearsonR,
      platform = df_heatmap$assayDetail,
      col = list(platform = platform_col, biomaterial = biomat_col, tumor = tumor_col, source = source_col),
      biomaterial = df_heatmap$biomaterial,
      tumor = df_heatmap$TumorType,
      source  = df_heatmap$source,
      annotation_name_side = "bottom", which = "row", show_legend = TRUE,
      width = unit(6, "cm"),
      gap = unit(0.5, "mm"),
      show_annotation_name = TRUE,
      simple_anno_size = unit(0.8, "cm"),
      annotation_legend_param = 
         list(
            platform = list(
               title = "platform"
            ),
            biomaterial = list(
               title = "biomaterial"
            ),
            tumor = list(
               title = "tumor", 
               ncol = 1
            ),
            source = list(
               title = "source", 
               ncol = 1
            )
         ))
   
   loss <- wes_palette("Zissou1")[1]
   gain <- wes_palette("Zissou1")[5]
   neutral <- "#F1F1F1"
   hm_q <- quantile(df_heatmap[,12:ncol(df_heatmap)], na.rm = TRUE, probs = c(0.01, 0.99))
   col_fun = colorRamp2(c(hm_q[1], 0, hm_q[2]), c(loss, neutral, gain))
   

   
   if (arrangehm == "pearsonR"){
      hm_df <- as.matrix(df_heatmap[,12:ncol(df_heatmap)])
      rownames(hm_df) <- as.factor(df_heatmap$pearsonR)
      arr <- order((rownames(hm_df)))
      split <- c(rep(1, nrow(hm_df)/2), rep(2, nrow(hm_df)/2))
      row_gap <- 5
      filename <- "_pearsonR_"
      hm_df <- hm_df[,2:ncol(hm_df)]
   } else {
      hm_df <- as.matrix(df_heatmap[,13:ncol(df_heatmap)])
      rownames(hm_df) <- as.factor(df_heatmap$pearsonR)
      arr <- NULL
      split <- df_heatmap$PatientID
      row_gap <- 0.4
      filename <- "_bytumor_"
   }
   ht <- Heatmap(hm_df, 
                 name = "log2ratio", column_split = colnames_hm, row_split = split,
                 col =   col_fun,
                 row_title = FALSE,
                 row_gap = unit(row_gap, "mm"),
                 cluster_columns = FALSE, 
                 cluster_rows = FALSE,
                 show_column_names = FALSE,
                 show_row_names = TRUE, 
                 row_labels = df_heatmap$PatientID, 
                 row_order = arr,
                 right_annotation = ha,
                 column_title_gp = gpar(fontsize = 10),
                 column_gap = unit(1, "mm"))
   png(paste0(plotfolder, "heatmap", filename, sample_select[1], ".png"), width = width_px, height = height_px, pointsize = 14 )
   map <- draw(ht, heatmap_legend_side = "bottom")
   dev.off()
   
}
library(data.table)

# to add middle 10
#pearson_vect <- c(sample_annotation_long %>% arrange(desc(pearsonR)) %>% head(n = 20) %>% pull(PatientID), setorder(data.table(sample_annotation_long), pearsonR)[(.N/2 - 20/2):(.N/2 + 20/2 - 1), ] %>% pull(PatientID), sample_annotation_long %>% arrange(desc(pearsonR)) %>% filter(!is.na(pearsonR)) %>% tail(n = 20) %>% pull(PatientID))

pearson_vect <- c(sample_annotation_long %>% filter(PatientID != "patient_101") %>% arrange(desc(pearsonR)) %>% head(n = 30) %>% pull(PatientID), sample_annotation_long %>% filter(PatientID != "patient_101") %>% arrange(desc(pearsonR)) %>% filter(!is.na(pearsonR)) %>% tail(n = 30) %>% pull(PatientID))

makeHeatmap(df_heatmap_init = df_heatmap_init, pearson_vect, width_px = 1200, height_px = 1400, arrangehm = "pearsonR")
```

## Adrenal tumors

```{r}
makeHeatmap(df_heatmap_init = df_heatmap_init, c("neuroblastoma", "ganglioneuroblastoma"), width_px = 1200, height_px = 2200)
```

![heatmap neuroblastoma and ganglioneuroblastoma](/Users/rmvpaeme/Repos/RVPCVP2012_sWGS/plots/heatmap_bytumor_neuroblastoma.png)

## Sarcomas

```{r}
makeHeatmap(df_heatmap_init = df_heatmap_init, c("osteosarcoma", "Ewing sarcoma", "rhabdomyosarcoma"), width_px = 1200, height_px = 1000)
```


![heatmap rhabdomyosarcoma, Ewing sarcoma and rhabdomyosarcoma](/Users/rmvpaeme/Repos/RVPCVP2012_sWGS/plots/heatmap_bytumor_osteosarcoma.png)

## Kidney tumors

```{r}
makeHeatmap(df_heatmap_init = df_heatmap_init, c("nephroblastoma", "kidney sarcoma", "malignant rhabdoid tumor of the kidney", "renal cell carcinoma"), width_px = 1200, height_px = 1200)
```

![heatmap nephroblastoma, kidney sarcoma, MRTK and RCC](/Users/rmvpaeme/Repos/RVPCVP2012_sWGS/plots/heatmap_bytumor_nephroblastoma.png)

## Brain tumors

```{r}
makeHeatmap(df_heatmap_init = df_heatmap_init, c("medulloblastoma", "ATRT", "PNET", "astrocytic pilocytoma", "ependymoma", "ganglioglioma", "glioblastoma", "hemangioblastoma", "meningeoma"), width_px = 1200, height_px = 800)
```

![heatmap brain tumors](/Users/rmvpaeme/Repos/RVPCVP2012_sWGS/plots/heatmap_bytumor_medulloblastoma.png)


# Hetereogeneity plots

![example heterogeneity plot before pictures of resection are added](/Users/rmvpaeme/Repos/RVPCVP2012_sWGS/plots/patient_057_CFD1806852_2G05_heterogen.png)


```{r, include = FALSE, echo = FALSE}
datadir_sWGS_cfDNA <- "./data/sWGS_cfDNA/"
datadir_sWGS_tissue <- "./data/sWGS_tissue/"
datadir_arrayBE <- "./data/array/"
datadir_arrayCZ <- "./data/SNParray/"
binsize = "200kb"

datadir1 = datadir_sWGS_cfDNA
datadir2 = datadir_arrayCZ
sample1 = "CFD1806851"
sample2 = "2S88"
sample3 = "2S89"
sample4 = "2S90"

library(ggtext)
makeCNVcomparison <- function(datadir1, datadir2, sample1, sample2, sample3, sample4, patientID){
   
   
   bins_sample1 <- read_tsv(paste0(datadir1, str_subset(dir(datadir1, pattern = sample1), "bins_mask")), col_types = c("fdd?d"),
                            col_names = c("chr", "start", "end", "id", "ratio"), skip = 1)
   bins_sample1 <- bins_sample1 %>% filter(!chr %in% c("X", "Y", "23", "24"))
   
   
   bins_sample2 <-  read_tsv(paste0(datadir2,"/",sample2,"_", binsize, ".tsv"), col_types = c("fddd"),
                             col_names = c("chr", "start", "end", "ratio"))
   bins_sample2 <- bins_sample2 %>% filter(!chr %in% c("X", "Y", "23", "24"))
   bins_sample2$chr <- factor(bins_sample2$chr, levels = chr_order)
   
   bins_sample3 <-  read_tsv(paste0(datadir2,"/",sample3,"_", binsize, ".tsv"), col_types = c("fddd"),
                             col_names = c("chr", "start", "end", "ratio"))
   bins_sample3 <- bins_sample3 %>% filter(!chr %in% c("X", "Y", "23", "24"))
   bins_sample3$chr <- factor(bins_sample3$chr, levels = chr_order)
   
   bins_sample4 <-  read_tsv(paste0(datadir2,"/",sample4,"_", binsize, ".tsv"), col_types = c("fddd"),
                             col_names = c("chr", "start", "end", "ratio"))
   bins_sample4 <- bins_sample4 %>% filter(!chr %in% c("X", "Y", "23", "24"))
   bins_sample4$chr <- factor(bins_sample4$chr, levels = chr_order)
   
   segs_sample1 <- read_tsv(paste0(datadir1, str_subset(dir(datadir1, pattern = sample1), "segments_mask.bed")), col_types = c("fdddd"),
                            col_names = c("chr", "start", "end", "ratio", "zscore"))
   segs_sample1 <- segs_sample1 %>% filter(!chr %in% c("X", "Y", "23", "24")) %>% mutate(Sample = sample1)
   
   
   segs_sample2 <- read_tsv(paste0(datadir2,"/",sample2,"_segments_per_", binsize, "_mask.tsv"), col_types = c("fddd"),
                            col_names = c("chr", "start", "end", "ratio"), skip = 1)
   
   segs_sample2 <- segs_sample2 %>% filter(!chr %in% c("X", "Y", "23", "24")) %>% mutate(Sample = sample2)
   segs_sample2$chr <- factor(segs_sample2$chr, levels = chr_order)
   
   segs_sample3 <- read_tsv(paste0(datadir2,"/",sample3,"_segments_per_", binsize, "_mask.tsv"), col_types = c("fddd"),
                            col_names = c("chr", "start", "end", "ratio"), skip = 1)
   
   segs_sample3 <- segs_sample3 %>% filter(!chr %in% c("X", "Y", "23", "24")) %>% mutate(Sample = sample2)
   segs_sample3$chr <- factor(segs_sample3$chr, levels = chr_order)
   
   segs_sample4 <- read_tsv(paste0(datadir2,"/",sample4,"_segments_per_", binsize, "_mask.tsv"), col_types = c("fddd"),
                            col_names = c("chr", "start", "end", "ratio"), skip = 1)
   
   segs_sample4 <- segs_sample4 %>% filter(!chr %in% c("X", "Y", "23", "24")) %>% mutate(Sample = sample2)
   segs_sample4$chr <- factor(segs_sample4$chr, levels = chr_order)
   
   color_cfDNA <- wes_palette("Cavalcanti1")[1]
   color_tumorDNA <- wes_palette("Cavalcanti1")[4]
   color_abberations <- wes_palette("Cavalcanti1")[5]
   
   col_s1 <- wes_palette("Royal1")[1]
   col_s2 <- wes_palette("Royal1")[2]
   col_s3 <- wes_palette("Royal2")[4]
   col_s4 <- wes_palette("Royal1")[4]
   
   
   cpa_s1 <- round(get.cpa.modified2(segs_sample1),4)
   cpa_s2 <- round(get.cpa.modified2(segs_sample2),4)
   cpa_s3 <- round(get.cpa.modified2(segs_sample3),4)
   cpa_s4 <- round(get.cpa.modified2(segs_sample4),4)
   
   corplot_df_bins <- inner_join(bins_sample1, bins_sample2, by = c("chr", "start", "end"))
   corplot_df_bins <- inner_join(corplot_df_bins, bins_sample3, by = c("chr", "start", "end"))
   corplot_df_bins <- inner_join(corplot_df_bins, bins_sample4, by = c("chr", "start", "end"))
   #corplot_df <- corplot_df %>% filter(!is.na(ratio.x) & !is.na(ratio.y))
   
   corplot_df_bins <- corplot_df_bins %>%
      mutate(rM.s1=rollapply(ratio.x,100, FUN=function(x) mean(x, na.rm=TRUE), fill=NA, align="right")) %>%
      mutate(rM.s2=rollapply(ratio.y,100, FUN=function(x) mean(x, na.rm=TRUE), fill=NA, align="right"))  %>%
      mutate(rM.s3=rollapply(ratio.x.x,100, FUN=function(x) mean(x, na.rm=TRUE), fill=NA, align="right"))  %>%
      mutate(rM.s4=rollapply(ratio.y.y,100, FUN=function(x) mean(x, na.rm=TRUE), fill=NA, align="right")) 
   
   max.ratio_roll <- max(c(corplot_df_bins$ratio.x, corplot_df_bins$ratio.y, corplot_df_bins$ratio.x.x, corplot_df_bins$ratio.y.y), na.rm = TRUE)
   min.ratio_roll <- min(c(corplot_df_bins$ratio.x, corplot_df_bins$ratio.y, corplot_df_bins$ratio.x.x, corplot_df_bins$ratio.y.y), na.rm = TRUE)
   
   colors <- c("cfDNA" = col_s1, "location 1" = col_s2, "location 2" = col_s3, "location 3" = col_s4)
   
   #patientID_title <- gsub("p", "P", patientID)
   patientID_title <- gsub("_", " ", patientID)
   
   rollmean <- ggplot(tibble(corplot_df_bins), aes(x = start)) + 
      theme_bw() + 
      labs(y = "log2(ratio)", x = "chromosomes") + lims(y = c(-1,1)) +
      geom_line(aes(y=rM.s1, col = "cfDNA"), alpha = 1, size = 0.6) +
      geom_line(aes(y=rM.s2, col = "location 1"), alpha = 0.8, size = 0.6) +
      geom_line(aes(y=rM.s3, col = "location 2"), alpha = 1, size = 0.6) +
      geom_line(aes(y=rM.s4, col = "location 3"), alpha = 0.8, size = 0.6) +
      theme(panel.spacing.x = unit(0, "lines"),
            panel.spacing.y = unit(0, "lines"),
            #  axis.title.x =  element_blank(),
            axis.text.x =  element_blank(),
            axis.ticks.x =  element_blank(),
            strip.background = element_rect(color = "white", fill = "white"),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.background = element_blank()) + 
      geom_hline(yintercept = 0, linetype = "dashed", col = "gray") +
      facet_wrap(~chr, strip.position = "bottom", scales ="free_x", nrow = 1)+labs(col = NULL) +
      scale_color_manual(values = colors) + theme(legend.position = "bottom") +
      labs(
         title = paste0(patientID_title, "
                      \n",
                      "<span> CPAm:
       <span style='color:",col_s1 ,";'>", round(cpa_s1,2), "</span> - 
       <span style='color:",col_s2 ,";'>", round(cpa_s2,2), "</span> -
       <span style='color:",col_s3 ,";'>", round(cpa_s3,2), "</span> -
       <span style='color:",col_s4 ,";'>", round(cpa_s4,2), "</span>
       </span>"
         ))+
      theme(
         plot.title = element_markdown(lineheight = 1.1),
         legend.text = element_markdown(size = 15),
         legend.key.size = unit(3,"line")
      )
   # + lims(y = c(-NA,NA))
   
   
   ggsave(paste0(plotfolder, patientID, "_", sample1, "_", sample2, "_heterogen.png"), plot = rollmean, width = 10, height = 4, dpi = 300)
}

datadir1 = datadir_sWGS_cfDNA
datadir2 = datadir_arrayCZ
sample1 = "CFD1806851"
sample2 = "2S88"
sample3 = "2S89"
sample4 = "2S90"
patientID = sample_annotation_full %>% filter(tumorDNA_ID == sample2) %>% pull(PatientID)
makeCNVcomparison(datadir1, datadir2, sample1, sample2, sample3, sample4, patientID)

sample1 = "CFD1806852"
sample2 = "2G05"
sample3 = "2G06"
sample4 = "2G07"
patientID = sample_annotation_full %>% filter(tumorDNA_ID == sample2) %>% pull(PatientID)
makeCNVcomparison(datadir1, datadir2, sample1, sample2, sample3, sample4, patientID)



makeCNVcomparison <- function(datadir1, datadir2, sample1, sample2, sample3, sample4){
   
   bins_sample1 <- read_tsv(paste0(datadir1, str_subset(dir(datadir1, pattern = sample1), "bins_mask")), col_types = c("fdd?d"),
                            col_names = c("chr", "start", "end", "id", "ratio"), skip = 1)
   bins_sample1 <- bins_sample1 %>% filter(!chr %in% c("X", "Y", "23", "24"))
   
   
   bins_sample2 <- read_tsv(paste0(datadir2, str_subset(dir(datadir2, pattern = sample2), "bins_mask")), col_types = c("fdd?d"),
                            col_names = c("chr", "start", "end", "id", "ratio"), skip = 1)
   bins_sample2 <- bins_sample2 %>% filter(!chr %in% c("X", "Y", "23", "24"))
   bins_sample2$chr <- factor(bins_sample2$chr, levels = chr_order)
   
   bins_sample3 <-  read_tsv(paste0(datadir2, str_subset(dir(datadir2, pattern = sample3), "bins_mask")), col_types = c("fdd?d"),
                             col_names = c("chr", "start", "end", "id", "ratio"), skip = 1)
   bins_sample3 <- bins_sample3 %>% filter(!chr %in% c("X", "Y", "23", "24"))
   bins_sample3$chr <- factor(bins_sample3$chr, levels = chr_order)
   
   bins_sample4 <-  read_tsv(paste0(datadir2, str_subset(dir(datadir2, pattern = sample4), "bins_mask")), col_types = c("fdd?d"),
                             col_names = c("chr", "start", "end", "id", "ratio"), skip = 1)
   bins_sample4 <- bins_sample4 %>% filter(!chr %in% c("X", "Y", "23", "24"))
   bins_sample4$chr <- factor(bins_sample4$chr, levels = chr_order)
   
   color_cfDNA <- wes_palette("Cavalcanti1")[1]
   color_tumorDNA <- wes_palette("Cavalcanti1")[4]
   color_abberations <- wes_palette("Cavalcanti1")[5]
   
   col_s1 <- wes_palette("Royal1")[1]
   col_s2 <- wes_palette("Royal1")[2]
   col_s3 <- wes_palette("Royal2")[4]
   col_s4 <- wes_palette("Royal1")[4]
   max.ratio_bins <- max(c(bins_sample1$ratio, bins_sample2$ratio, bins_sample3$ratio, bins_sample4$ratio), na.rm = TRUE)
   min.ratio_bins <- min(c(bins_sample1$ratio, bins_sample2$ratio, bins_sample3$ratio, bins_sample4$ratio), na.rm = TRUE)
   
   
   corplot_df_bins <- full_join(bins_sample1, bins_sample2, by = c("chr", "start", "end"))
   corplot_df_bins <- full_join(corplot_df_bins, bins_sample3, by = c("chr", "start", "end"))
   corplot_df_bins <- full_join(corplot_df_bins, bins_sample4, by = c("chr", "start", "end"))
   #corplot_df <- corplot_df %>% filter(!is.na(ratio.x) & !is.na(ratio.y))
   
   corplot_df_bins <- corplot_df_bins %>%
      mutate(rM.s1=rollapply(ratio.x,100, FUN=function(x) mean(x, na.rm=TRUE), fill=NA, align="right")) %>%
      mutate(rM.s2=rollapply(ratio.y,100, FUN=function(x) mean(x, na.rm=TRUE), fill=NA, align="right"))  %>%
      mutate(rM.s3=rollapply(ratio.x.x,100, FUN=function(x) mean(x, na.rm=TRUE), fill=NA, align="right"))  %>%
      mutate(rM.s4=rollapply(ratio.y.y,100, FUN=function(x) mean(x, na.rm=TRUE), fill=NA, align="right")) 
   
   
   colors <- c("EDTA plasma" = col_s1, "Streck plasma" = col_s2, "CSF" = col_s3, "Streck CSF" = col_s4)
   
   rollmean <- ggplot(tibble(corplot_df_bins) %>% filter(chr == "6" | chr == "5" | chr == "7"), aes(x = start)) + 
      theme_bw() + 
      labs(y = "log2(ratio)", x = "chromosomes") + lims(y = c(-1,1)) +
      geom_line(aes(y=rM.s3, col = "EDTA plasma"), alpha = 0.7, size = 0.6) +
      geom_line(aes(y=rM.s4, col = "Streck plasma"), alpha = 0.7, size = 0.6) +
      geom_line(aes(y=rM.s1, col = "CSF"), alpha = 0.7, size = 0.6) +
      geom_line(aes(y=rM.s2, col = "Streck CSF"), alpha = 0.7, size = 0.6) +
      theme(panel.spacing.x = unit(0, "lines"),
            panel.spacing.y = unit(0, "lines"),
            #  axis.title.x =  element_blank(),
            axis.text.x =  element_blank(),
            axis.ticks.x =  element_blank(),
            strip.background = element_rect(color = "white", fill = "white"),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.background = element_blank()) + 
      geom_hline(yintercept = 0, linetype = "dashed", col = "gray") +
      facet_wrap(~chr, strip.position = "bottom", scales ="free_x", nrow = 1)+labs(col = NULL) +
      scale_color_manual(values = colors) + theme(legend.position = "bottom") + lims(y = c(-1,0.2))
   
   
   rollmean <- rollmean + theme(legend.text=element_text(size=15)) + theme(legend.key.size = unit(3,"line"))
   ggsave(paste0(plotfolder, patientID, "_", sample1, "_", sample2, "_CSF.png"), plot = rollmean, width = 10, height = 4, dpi = 300)
}

datadir1 = datadir_sWGS_cfDNA
datadir2 = datadir_sWGS_cfDNA
sample1 = "CFD1802291"
sample2 = "CFD1802292"
sample3 = "M1A800108"
sample4 = "MMA1800016"
makeCNVcomparison(datadir1, datadir2, sample1, sample2, sample3, sample4)
```

# Manuscript figures

```{r, fig.width = 6, fig.height= 8}
p <- ggarrange(ridgeplot_cfDNA_per_tumor, ridgeplot_tissueDNA_per_tumor, qual_interaction  + 
                  theme(strip.text.x = element_blank(),
                        strip.background = element_rect(colour="white", fill="white"),
                        legend.position=c(0.3,0.85), legend.title = element_text(size = 8),
                  )  +
                  scale_color_continuous(breaks = c(0, 10, 20), labels = c("1", "10", "100")) + labs(title = NULL, col = "cfDNA/HMW ratio"), ncol = 3, labels = c("B", "C", "D"), widths = c(0.30,0.20, 0.30))

p_arr <- ggarrange(p_CPAvsR, p, nrow = 2, labels = c("A"), common.legend = FALSE)

ggsave("./plots/facetplot.png", plot = ggplot2::last_plot(), dpi = 300, width = 9, height = 13)
```

# Result section in RMarkdown
The results section was written in Rmarkdown, the numbers were pulled immediately from the dataframes in this RMarkdown file. For the code, see the `.Rmd` file (as opposed to the `.html` file).

**Sample collection.** We retrospectively included `r (cfDNAvsTissue %>% pull(CFD_ID) %>% unique() %>% length() + cfDNAvsTissue %>% pull(tumorDNA_ID) %>% unique() %>% length())` unique samples (n = `r cfDNAvsTissue %>% filter(CFD_biomaterial == "plasma") %>% pull(CFD_ID) %>% unique() %>% length()` plasma, n = `r cfDNAvsTissue %>% filter(CFD_biomaterial == "CSF") %>% pull(CFD_ID) %>% unique() %>% length()` CSF, n = `r cfDNAvsTissue %>% pull(tumorDNA_ID) %>% unique() %>% length()` tumor tissue) of `r cfDNAvsTissue %>% select(PatientID) %>% unique %>% nrow()` unique pediatric cancer cases. Patients were recruited at Ghent University Hospital (n = `r cfDNAvsTissue %>% filter(SampleOrigin == "UZG") %>% select(PatientID) %>% unique %>% nrow()`), Princess Máxima Center (n = `r cfDNAvsTissue %>% filter(SampleOrigin == "PMC") %>% select(PatientID) %>% unique %>% nrow()`), Institut Curie (n = `r cfDNAvsTissue %>% filter(SampleOrigin == "CURIE") %>% select(PatientID) %>% unique %>% nrow()`) and University Hospital Motol (n = `r cfDNAvsTissue %>% filter(SampleOrigin == "MH") %>% select(PatientID) %>% unique %>% nrow()`). In total, the cohort comprised Ewing sarcoma (n = `r cfDNAvsTissue %>% filter(TumorType == "Ewing sarcoma") %>% select(PatientID) %>% unique %>% nrow()`), osteosarcoma (n = `r cfDNAvsTissue %>% filter(TumorType == "osteosarcoma") %>% select(PatientID) %>% unique %>% nrow()`), rhabdomyosarcoma (n = `r cfDNAvsTissue %>% filter(TumorType == "rhabdomyosarcoma") %>% select(PatientID) %>% unique %>% nrow()`), nephroblastoma (n = `r cfDNAvsTissue %>% filter(TumorType == "nephroblastoma") %>% select(PatientID) %>% unique %>% nrow()`), neuroblastoma (n = `r cfDNAvsTissue %>% filter(TumorType == "neuroblastoma") %>% select(PatientID) %>% unique %>% nrow()`) and brain tumor samples (n = `r cfDNAvsTissue %>% filter(TumorGroup == "brain tumor") %>% select(PatientID) %>% unique %>% nrow()`). More detailed sample information is summarized in supplementary table X. From these `r cfDNAvsTissue %>% select(PatientID) %>% unique %>% nrow()` patients, copy number aberrations (CNAs) were measured in plasma with shallow whole genome sequencing (sWGS) in all samples, while on tissue this was done either with sWGS (n = `r cfDNAvsTissue %>% filter(tumorDNA_assay_detail == "sWGS") %>% select(tumorDNA_ID) %>% unique %>% nrow()`), WES (n = `r cfDNAvsTissue %>% filter(tumorDNA_assay_detail == "WES") %>% select(tumorDNA_ID) %>% unique %>% nrow()`) or array CGH (n = `r cfDNAvsTissue %>% filter(tumorDNA_assay_detail != "sWGS" & tumorDNA_assay_detail != "WES"  ) %>% select(UniqueID) %>% unique %>% nrow()`). In case of sWGS, `r round((mapped_reads %>% filter(biomaterial == "cfDNA") %>% pull(raw_total_sequences) %>% median())/1e6,2)`M [`r (mapped_reads %>% filter(biomaterial == "cfDNA") %>% pull(raw_total_sequences) %>% quantile())[2]`-`r (mapped_reads %>% filter(biomaterial == "cfDNA") %>% pull(raw_total_sequences) %>% quantile())[4]`] reads were generated for cfDNA and `r round((mapped_reads %>% filter(biomaterial == "tumor DNA") %>% pull(raw_total_sequences) %>% median())/1e6,2)`M [`r (mapped_reads %>% filter(biomaterial == "tumor DNA") %>% pull(raw_total_sequences) %>% quantile())[2]`-`r (mapped_reads %>% filter(biomaterial == "tumor DNA") %>% pull(raw_total_sequences) %>% quantile())[4]`] for tissue DNA, with `r round((duplicated_reads %>% filter(biomaterial == "cfDNA") %>% pull(duplicate_percentage) %>% median()),2)`% [`r (duplicated_reads %>% filter(biomaterial == "cfDNA") %>% pull(duplicate_percentage) %>% quantile())[2]`-`r (duplicated_reads %>% filter(biomaterial == "cfDNA") %>% pull(duplicate_percentage) %>% quantile())[4]`] and `r round((duplicated_reads %>% filter(biomaterial == "tumor DNA") %>% pull(duplicate_percentage) %>% median()),2)`% duplicate reads [`r (duplicated_reads %>% filter(biomaterial == "tumor DNA") %>% pull(duplicate_percentage) %>% quantile())[2]`-`r (duplicated_reads %>% filter(biomaterial == "tumor DNA") %>% pull(duplicate_percentage) %>% quantile())[4]`], respectively.


**Copy number abnormality is higher in tumor tissue than in plasma.** For every sample, the modified copy number profile abnormality (CPAm) score was calculated (see Methods for details, see figure ##2A and ##2B for an illustration of the relationship between the genome-wide copy number profile and the CPAm score). The median CPAm across all tumor types was found to be `r median(cfDNAvsTissue$cpa_cfDNA)` [`r quantile(cfDNAvsTissue$cpa_cfDNA)[2]`-`r quantile(cfDNAvsTissue$cpa_cfDNA)[4]`] in cfDNA and `r median(cfDNAvsTissue$cpa_tumorDNA)` [`r quantile(cfDNAvsTissue$cpa_tumorDNA)[2]`-`r quantile(cfDNAvsTissue$cpa_tumorDNA)[4]`]  in tissue DNA (figure ###4B illustrates the CPAm score per tumor type). Based on manual inspection (tissue DNA) or the previously established 1% FDR threshold for CPAm (cfDNA, see Methods), we found that `r cfDNAvsTissue %>% filter(CNA_cfDNA == "flat") %>% pull(CNA_cfDNA) %>% length()` (`r 100*cfDNAvsTissue %>% filter(CNA_cfDNA == "flat") %>% pull(CNA_cfDNA) %>% length() / cfDNAvsTissue %>% filter(CNA_cfDNA == "CNA" | CNA_cfDNA == "flat" ) %>% pull(CNA_cfDNA) %>% length()`%) cfDNA samples and `r (cfDNAvsTissue %>% filter(tumorDNA_CNAs_consensus == "FALSE") %>% pull(tumorDNA_CNAs_consensus) %>% length())` (`r  100*(cfDNAvsTissue %>% filter(tumorDNA_CNAs_consensus == "FALSE") %>% pull(tumorDNA_CNAs_consensus) %>% length()) / (cfDNAvsTissue %>% pull(tumorDNA_CNAs_consensus) %>% length())`%) tumor samples were labeled as “flat”, i.e., copy number neutral.  


**cfDNA sample quality and disease extent determines concordance between cfDNA and tissue DNA.** Previous studies have pointed at a substantial influence of cfDNA sample quality on the detection of tumor-derived DNA in cfDNA (ADD REFS). We assessed the cfDNA quality by determining the ratio of cfDNA (< 700 bp) vs. high molecular weight (> 700 bp) for `r cfDNAvsTissue %>% filter(!is.na(cfDNA_HMW_ratio)) %>% nrow()` samples (`r median(cfDNAvsTissue$cfDNA_HMW_ratio, na.rm = TRUE)` [`r quantile(cfDNAvsTissue$cfDNA_HMW_ratio, na.rm = TRUE)[2]`-`r quantile(cfDNAvsTissue$cfDNA_HMW_ratio, na.rm = TRUE)[4]`], figure ##1 and supplemental figures #XX). For every cfDNA-tissue pair, the Pearson R and the CPAm score (see Methods) was calculated and associated with the cfDNA/HMW ratio (figure XX, supplemental figures XX). 
<!-- The median cfDNA/HMW ratio of copy number neutral cfDNA samples was `r cfDNAvsTissue %>% filter(CNA_cfDNA == "flat") %>% pull(cfDNA_HMW_ratio ) %>% median(na.rm = TRUE)` [`r quantile(cfDNAvsTissue %>% filter(CNA_cfDNA == "flat") %>% pull(cfDNA_HMW_ratio ), na.rm = TRUE)[2]` - `r quantile(cfDNAvsTissue %>% filter(CNA_cfDNA == "flat") %>% pull(cfDNA_HMW_ratio ),na.rm = TRUE)[4]`] versus `r cfDNAvsTissue %>% filter(CNA_cfDNA == "CNA") %>% pull(cfDNA_HMW_ratio ) %>% median(na.rm = TRUE)` [`r (cfDNAvsTissue %>% filter(CNA_cfDNA == "CNA") %>% pull(cfDNA_HMW_ratio ) %>% quantile(na.rm = TRUE))[2]` - `r (cfDNAvsTissue %>% filter(CNA_cfDNA == "CNA") %>% pull(cfDNA_HMW_ratio ) %>% quantile(na.rm = TRUE))[4]`] for samples above the 1% FDR threshold (p < `r wilcox.test(cfDNAvsTissue %>% filter(CNA_cfDNA == "CNA") %>% pull(cfDNA_HMW_ratio ), cfDNAvsTissue %>% filter(CNA_cfDNA == "flat") %>% pull(cfDNA_HMW_ratio ))[3]`, Wilcoxon Rank Sum test).  -->
In Figure XX, we observed an apparent influence of cfDNA/HMW ratio and the tissue assay (i.e. Illumina BeadChip, sWGS, WES) on the copy number load in cfDNA. Subsequently, we more deeply investigated the effect of these parameters on the agreement between the tissue CNAs and cfDNA CNAs. Using a generalized additive model (GAM), we found that a higher cfDNA/HMW ratio (after log10 transformation) was associated with a better agreement between tissue CNA and cfDNA CNAs (p < `r summary(Model)$p.pv["L2R_tumorDNA:cfDNA_HMW_ratio_log10"] + 0.001`), after adjusting for tumor type, disease extent and the platform on which the tissue copy number was determined (e.g. sWGS or Illumina BeadChip, full model in supplemental data X). Based on previously-defined thresholds (ref epigenetics), we found that of on a total of `r cfDNAvsTissue %>% filter(!is.na(cfDNA_HMW_ratio)) %>% select(PatientID) %>% nrow()` samples, the `r cfDNAvsTissue %>% filter(cfDNA_HMW_ratio < 1) %>% nrow()` samples with a cfDNA/HMW ratio lower than 1 (low quality) contained `r cfDNAvsTissue %>% filter(cfDNA_HMW_ratio < 1) %>% filter(CNA_cfDNA == 'flat') %>% nrow()` (`r cfDNAvsTissue %>% filter(cfDNA_HMW_ratio < 1) %>% filter(CNA_cfDNA == 'flat') %>% nrow() / cfDNAvsTissue %>% filter(cfDNA_HMW_ratio < 1) %>% nrow() *100`%) copy number neutral samples in cfDNA while of these `r cfDNAvsTissue %>% filter(cfDNA_HMW_ratio < 1) %>% filter(CNA_cfDNA == 'flat') %>% nrow()` cfDNA neutral samples, there were `r cfDNAvsTissue %>% filter(cfDNA_HMW_ratio < 1) %>% filter(tumorDNA_CNAs_consensus == 'TRUE') %>% nrow()` samples with CNAs in the tumor. Of `r cfDNAvsTissue %>% filter(cfDNA_HMW_ratio >= 1 & cfDNA_HMW_ratio < 5) %>% nrow()` samples with a ratio between 1 and 5 (intermediate quality) `r cfDNAvsTissue %>% filter(cfDNA_HMW_ratio >= 1 & cfDNA_HMW_ratio < 5) %>% filter(CNA_cfDNA == 'flat') %>% nrow()` (`r cfDNAvsTissue %>% filter(cfDNA_HMW_ratio >= 1 & cfDNA_HMW_ratio < 5) %>% filter(CNA_cfDNA == 'flat') %>% nrow() / cfDNAvsTissue %>% filter(cfDNA_HMW_ratio >= 1 & cfDNA_HMW_ratio < 5) %>% nrow() *100`%) were copy number neutral (all corresponding tumor samples contained CNAs). Finally, of `r cfDNAvsTissue %>% filter(cfDNA_HMW_ratio >= 5) %>% nrow()` cfDNA samples with a ratio more than 5 (high quality), `r cfDNAvsTissue %>% filter(cfDNA_HMW_ratio >= 5) %>% filter(CNA_cfDNA == 'flat') %>% nrow()` (`r cfDNAvsTissue %>% filter(cfDNA_HMW_ratio >= 5) %>% filter(CNA_cfDNA == 'flat') %>% nrow() / cfDNAvsTissue %>% filter(cfDNA_HMW_ratio >= 5) %>% nrow() *100`%) were copy number neutral. Upon closer inspection of these three cases, the tumor was also copy number neutral in one (patient 008) and contained segmental aberrations in the other two cases (patient 044, patient 206). Overall, disagreement (i.e. tissue DNA containing CNAs while the plasma does not or vice versa) was seen in `r cfDNAvsTissue %>% filter(tumorDNA_CNAs_consensus == "FALSE" & CNA_cfDNA == 'CNA') %>% nrow()` samples and `r cfDNAvsTissue %>% filter(tumorDNA_CNAs_consensus == "TRUE" & CNA_cfDNA == 'flat') %>% nrow()` samples, respectively. The cfDNA/HMW ratio in these `r cfDNAvsTissue %>% filter(tumorDNA_CNAs_consensus == "FALSE" & CNA_cfDNA == 'CNA') %>% nrow() + cfDNAvsTissue %>% filter(tumorDNA_CNAs_consensus == "TRUE" & CNA_cfDNA == 'flat') %>% nrow()` discordant samples is  `r round((cfDNAvsTissue %>% filter(tumorDNA_CNAs_consensus == "TRUE" & CNA_cfDNA == 'flat' | tumorDNA_CNAs_consensus == "FALSE" & CNA_cfDNA == 'CNA')  %>% pull(cfDNA_HMW_ratio) %>% median(na.rm = TRUE)),2)` [`r (cfDNAvsTissue %>% filter(tumorDNA_CNAs_consensus == "TRUE" & CNA_cfDNA == 'flat' | tumorDNA_CNAs_consensus == "FALSE" & CNA_cfDNA == 'CNA')  %>% pull(cfDNA_HMW_ratio) %>% quantile(na.rm = TRUE))[2]`-`r (cfDNAvsTissue %>% filter(tumorDNA_CNAs_consensus == "TRUE" & CNA_cfDNA == 'flat' | tumorDNA_CNAs_consensus == "FALSE" & CNA_cfDNA == 'CNA')  %>% pull(cfDNA_HMW_ratio) %>% quantile(na.rm = TRUE))[4]`], while the ratio in the concordant samples is  `r round((cfDNAvsTissue %>% filter(tumorDNA_CNAs_consensus == "FALSE" & CNA_cfDNA == 'flat' | tumorDNA_CNAs_consensus == "TRUE" & CNA_cfDNA == 'CNA')  %>% pull(cfDNA_HMW_ratio) %>% median(na.rm = TRUE)),2)` [`r (cfDNAvsTissue %>% filter(tumorDNA_CNAs_consensus == "TRUE" & CNA_cfDNA == 'CNA' | tumorDNA_CNAs_consensus == "FALSE" & CNA_cfDNA == 'flat')  %>% pull(cfDNA_HMW_ratio) %>% quantile(na.rm = TRUE))[2]`-`r (cfDNAvsTissue %>% filter(tumorDNA_CNAs_consensus == "FALSE" & CNA_cfDNA == 'flat' | tumorDNA_CNAs_consensus == "TRUE" & CNA_cfDNA == 'CNA')  %>% pull(cfDNA_HMW_ratio) %>% quantile(na.rm = TRUE))[4]`].
Furthermore, based on the GAM, patients with metastatic disease had a higher agreements between the log2(ratio) in cfDNA and log2(ratio) in tissue DNA. 

**Spatial heterogeneity in tumor samples.** For two nephroblastoma cases, cfDNA was available at diagnosis and tumor tissue after treatment (patient XX and patient XX). Patient `r sample_annotation %>% filter(tumorDNA_ID == "2S88") %>% pull(PatientID) %>% gsub("patient_0", "", .)` and `r sample_annotation %>% filter(tumorDNA_ID == "2G07") %>% pull(PatientID) %>% gsub("patient_0", "", .)` were treated according to the SIOP Wilms tumor protocol for nephroblastoma (ADDS REFS). Plasma from these two patients was obtained before initiation of chemotherapy and tissue samples were obtained after 4 weeks of neoadjuvant chemotherapy. After resection, the copy number profile was determined in three different locations in the resected kidney and compared to the pre-treatment plasma sample (figure XXX2C), which revealed substantial intra-tumor difference and discordance with cfDNA (e.g. patient `r sample_annotation %>% filter(tumorDNA_ID == "2S88") %>% pull(PatientID) %>% gsub("patient_0", "", .)`, gain on chr12, patient `r sample_annotation %>% filter(tumorDNA_ID == "2G07") %>% pull(PatientID) %>% gsub("patient_0", "", .)` both present in cfDNA but not in all tumor sections, figure 2C). For patient `r sample_annotation %>% filter(tumorDNA_ID == "2S88") %>% pull(PatientID) %>% gsub("patient_0", "", .)`, histologic evaluation for locations 1 and 2 was determined to be triphasic nephroblastoma with necrosis and location 3 was kidney with blastema. For patient `r sample_annotation %>% filter(tumorDNA_ID == "2G07") %>% pull(PatientID) %>% gsub("patient_0", "", .)`, locations 1 and 3 were determined to be triphasic nephroblastoma with rhabdomyoblastic differentiation and location 2 was necrotic tissue. Importantly, gain of 1q, a prognostic biomarker in Wilms tumor (https://pubmed.ncbi.nlm.nih.gov/27432915/), was only observed in location 1 of patient `r sample_annotation %>% filter(tumorDNA_ID == "2S88") %>% pull(PatientID) %>% gsub("patient_0", "", .)` and location 2 and 3 of patient `r sample_annotation %>% filter(tumorDNA_ID == "2G07") %>% pull(PatientID) %>% gsub("patient_0", "", .)`, while not in cfDNA at diagnosis.

**CNAs can be unique for cfDNA or for tissue DNA.** Several samples, of moderate to high quality (cfDNA/HMW ratio above 1) and with a high CPAm (more than 3 times the CPAm at the 1% FDR threshold) in cfDNA were observed to have a low Pearson correlation coefficient with the respective tumor CPAm value (e.g. patient 079, patient 212, patient 077, patient 196). Upon closer inspection, several aberrations are discordant between plasma and tissue DNA (patient 077, patient 079, patient 196, figure XXX). Furthermore, other discordant samples were seen upon manual inspection, albeit with smaller and more subtle differences. In three neuroblastoma cases (patient 185, 136, 109) recurrent segmental CNAs (1p deletion, 2p gain, 11q deletion and 17q gain) were more clearly present in the cfDNA than in the tissue DNA. In one case (patient 212), only the amplification of MYCN on 2p24.3 could be detected in the tissue DNA while analysis of the cfDNA samples showed that many more CNAs were present (figure XXX). For several nephroblastoma cases (e.g. patient 035 and 056), chromosomal aberrations were identified in the cfDNA and not in the tissue DNA. 

**cfDNA is complementary to tissue DNA in the risk stratification of neuroblastoma and nephroblastoma.** As *MYCN* amplification is an important prognostic biomarker in neuroblastoma, we investigated agreement between *MYCN* gain/amplification between cfDNA and tissue DNA. On all samples (irrespective of the sample quality) (n = `r MYCN %>% nrow()`), *MYCN* calls were similar in cfDNA and tissue DNA without any discrepancies. In nephroblastoma, relying on cfDNA for determining the presence of 1q gain, a prognostic marker (ADD REF), `r table(nephr_1q$gain_1q_WT_cfDNA, nephr_1q$gain_1q_WT_tumor)[1,2]` samples (n = `r nephr_1q %>% nrow()`) (or `r table(nephr_1q_highqual$gain_1q_WT_cfDNA, nephr_1q_highqual$gain_1q_WT_tumor)[1,2]`/`r nephr_1q_highqual %>% nrow()` when only including the intermediate to high quality samples with a cfDNA/HMW ratio above 1) with 1q gain would have been missed, while only relying on tissue DNA 1q gain would have been missed in `r table(nephr_1q$gain_1q_WT_cfDNA, nephr_1q$gain_1q_WT_tumor)[2,1]` cases. 

**Cerebrospinal fluid is preferable to plasma for medulloblastoma** For brain tumors, it is expected that higher tissue DNA fractions will be found in cerebrospinal fluid (CSF) when compared to  plasma. We analyzed `r sample_annotation %>% filter(CFD_biomaterial == "CSF") %>% pull(PatientID) %>% unique() %>% length()` CSF samples of brain tumor patients. All showed clear CNAs in the CSF (almost) fully concordant to the matching tissue DNA profile (HEATMAP FIGURE). For patient XXX with a medulloblastoma, we had a matching plasma sample available, with a cfDNA/HMW ratio of 1.94, , with a cfDNA/HMW ratio of `r cfDNAvsTissue %>% filter(CFD_ID == "CFD1802291") %>% pull(cfDNA_HMW_ratio)` that presented a copy number neutral profile, while CSF depicted a chromosome 6 loss (supplemental figure XX).

